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Sample records for predicting disease risk

  1. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk)

    DEFF Research Database (Denmark)

    Hajifathalian, Kaveh; Ueda, Peter; Lu, Yuan

    2015-01-01

    BACKGROUND: Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be reca...

  2. Alzheimer's Disease: Genes, pathogenesis and risk prediction

    NARCIS (Netherlands)

    K. Sleegers (Kristel); C.M. van Duijn (Cornelia)

    2001-01-01

    textabstractWith the aging of western society the contribution to morbidity of diseases of the elderly, such as dementia, will increase exponentially. Thorough preventative and curative strategies are needed to constrain the increasing prevalence of these disabling diseases. Better understanding of

  3. Predicting disease risk using bootstrap ranking and classification algorithms.

    Directory of Open Access Journals (Sweden)

    Ohad Manor

    Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

  4. Predicting disease risks from highly imbalanced data using random forest

    Directory of Open Access Journals (Sweden)

    Chakraborty Sounak

    2011-07-01

    Full Text Available Abstract Background We present a method utilizing Healthcare Cost and Utilization Project (HCUP dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. Methods We employed the National Inpatient Sample (NIS data, which is publicly available through Healthcare Cost and Utilization Project (HCUP, to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM, bagging, boosting and RF to predict the risk of eight chronic diseases. Results We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC curve (AUC. In addition, RF has the advantage of computing the importance of each variable in the classification process. Conclusions In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.

  5. How to make predictions about future infectious disease risks

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    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  6. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  7. Predicting infection risk of airborne foot-and-mouth disease.

    Science.gov (United States)

    Schley, David; Burgin, Laura; Gloster, John

    2009-05-06

    Foot-and-mouth disease is a highly contagious disease of cloven-hoofed animals, the control and eradication of which is of significant worldwide socio-economic importance. The virus may spread by direct contact between animals or via fomites as well as through airborne transmission, with the latter being the most difficult to control. Here, we consider the risk of infection to flocks or herds from airborne virus emitted from a known infected premises. We show that airborne infection can be predicted quickly and with a good degree of accuracy, provided that the source of virus emission has been determined and reliable geo-referenced herd data are available. A simple model provides a reliable tool for estimating risk from known sources and for prioritizing surveillance and detection efforts. The issue of data information management systems was highlighted as a lesson to be learned from the official inquiry into the UK 2007 foot-and-mouth outbreak: results here suggest that the efficacy of disease control measures could be markedly improved through an accurate livestock database incorporating flock/herd size and location, which would enable tactical as well as strategic modelling.

  8. Value of multiple risk factors in predicting coronary artery disease

    International Nuclear Information System (INIS)

    Zhu Zhengbin; Zhang Ruiyan; Zhang Qi; Yang Zhenkun; Hu Jian; Zhang Jiansheng; Shen Weifeng

    2008-01-01

    Objective: This study sought to assess the relationship between correlative comprehension risk factors and coronary arterial disease and to build up a simple mathematical model to evaluate the extension of coronary artery lesion in patients with stable angina. Methods: A total of 1024 patients with chest pain who underwent coronary angiography were divided into CAD group(n=625)and control group(n=399) based on at least one significant coronary artery narrowing more than 50% in diameter. Independent risk factors for CAD were evaluated and multivariate logistic regression model and receiver-operating characteristic(ROC) curves were used to estimate the independent influence factor for CAD and built up a simple formula for clinical use. Results: Multivariate regression analysis revealed that UACR > 7.25 μg/mg(OR=3.6; 95% CI 2.6-4.9; P 20 mmol/L(OR=3.2; 95% CI 2.3-4.4; P 2 (OR=2.3; 95% CI 1.4-3.8; P 2.6 mmol/L (OR 2.141; 95% CI 1.586-2.890; P 7.25 μg/mg + 1.158 x hsCRP > 20 mmol/L + 0.891 GFR 2 + 0.831 x LVEF 2.6 mmol/L + 0.676 x smoking history + 0.594 x male + 0.459 x diabetes + 0.425 x hypertension). Area under the curve was 0.811 (P < 0.01), and the optimal probability value for predicting severe stage of CAD was 0.977 (sensitivity 49.0%, specificity 92.7% ). Conclusions: Risk factors including renal insufficiency were the main predictors for CAD. The logistic regression model is the non-invasive method of choice for predicting the extension of coronary artery lesion in patients with stable agiana. (authors)

  9. Resistance training and predicted risk of coronary heart disease in ...

    African Journals Online (AJOL)

    The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

  10. Risk Matrix for Prediction of Disease Progression in a Referral Cohort of Patients with Crohn's Disease.

    Science.gov (United States)

    Lakatos, Peter L; Sipeki, Nora; Kovacs, Gyorgy; Palyu, Eszter; Norman, Gary L; Shums, Zakera; Golovics, Petra A; Lovasz, Barbara D; Antal-Szalmas, Peter; Papp, Maria

    2015-10-01

    Early identification of patients with Crohn's disease (CD) at risk of subsequent complications is essential for adapting the treatment strategy. We aimed to develop a prediction model including clinical and serological markers for assessing the probability of developing advanced disease in a prospective referral CD cohort. Two hundred and seventy-one consecutive CD patients (42.4% males, median follow-up 108 months) were included and followed up prospectively. Anti-Saccharomyces cerevisiae antibodies (ASCA IgA/IgG) were determined by enzyme-linked immunosorbent assay. The final analysis was limited to patients with inflammatory disease behaviour at diagnosis. The final definition of advanced disease outcome was having intestinal resection or disease behaviour progression. Antibody (ASCA IgA and/or IgG) status, disease location and need for early azathioprine were included in a 3-, 5- and 7-year prediction matrix. The probability of advanced disease after 5 years varied from 6.2 to 55% depending on the combination of predictors. Similar findings were obtained in Kaplan-Meier analysis; the combination of ASCA, location and early use of azathioprine was associated with the probability of developing advanced disease (p < 0.001, log rank test). Our prediction models identified substantial differences in the probability of developing advanced disease in the early disease course of CD. Markers identified in this referral cohort were different from those previously published in a population-based cohort, suggesting that different prediction models should be used in the referral setting. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  11. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

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    Yiming Hu

    2017-06-01

    Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

  12. A genetic risk score combining ten psoriasis risk loci improves disease prediction.

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    Haoyan Chen

    2011-04-01

    Full Text Available Psoriasis is a chronic, immune-mediated skin disease affecting 2-3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS and a weighted (wGRS approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7 versus 12.09 (SD 1.8, p = 4.577×10(-40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63-14.57, p = 2.010×10(-65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC. The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10(-8. Additionally, the AUC for HLA-C alone (rs10484554 was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18, highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10(-6 and family history (p = 0.020. Using a liability threshold model, we estimated that the 10 risk loci account for only 11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.

  13. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

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    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-08-07

    Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  14. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-01-01

    Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

  15. Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

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    Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

    2015-01-01

    Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

  16. A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

    Science.gov (United States)

    Vinarti, Retno; Hederman, Lucy

    2018-01-01

    We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

  17. Cardiovascular risk prediction in chronic kidney disease patients

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    Santiago Cedeño Mora

    2017-05-01

    Conclusion: The cardiovascular risk scores (FRS-CVD and ASCVD [AHA/ACC 2013] can estimate the probability of atherosclerotic cardiovascular events in patients with CKD regardless of renal function, albuminuria and previous cardiovascular events.

  18. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

    DEFF Research Database (Denmark)

    Paige, Ellie; Barrett, Jessica; Pennells, Lisa

    2017-01-01

    The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data...

  19. Molecular prediction of disease risk and severity in a large Dutch Crohn's disease cohort

    NARCIS (Netherlands)

    Weersma, R.K.; Stokkers, P.C.F.; van Bodegraven, A.A.; van Hogezand, R.A.; Verspaget, H.W.; de Jong, D.J.; van der Woude, C.J.; Oldenburg, B.; Linskens, R.K.; Festen, E.A.M.; van der Steege, G.; Hommes, D.W.; Crusius, J.B.A.; Wijmenga, C.; Nolte, I.M.; Dijkstra, G.

    2009-01-01

    Background: Crohn's disease and ulcerative colitis have a complex genetic background. We assessed the risk for both the development and severity of the disease by combining information from genetic variants associated with inflammatory bowel disease (IBD). Methods: We studied 2804 patients (1684

  20. Development of a disease risk prediction model for downy mildew (Peronospora sparsa) in boysenberry.

    Science.gov (United States)

    Kim, Kwang Soo; Beresford, Robert M; Walter, Monika

    2014-01-01

    Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002).

  1. Risk prediction and risk reduction in patients with manifest arterial disease

    NARCIS (Netherlands)

    Goessens, B.M.B.; Goessens, B.M.B.

    2006-01-01

    Risicovoorspelling en risicoverlaging bij patienten met manifest vaatlijden Engelstalig abstract The number of patients with clinical manifest arterial disease is increasing because of the aging of the population. Patients with manifest arterial disease have an increased risk of a new vascular event

  2. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  3. Mortality Risk Prediction in Scleroderma-Related Interstitial Lung Disease: The SADL Model.

    Science.gov (United States)

    Morisset, Julie; Vittinghoff, Eric; Elicker, Brett M; Hu, Xiaowen; Le, Stephanie; Ryu, Jay H; Jones, Kirk D; Haemel, Anna; Golden, Jeffrey A; Boin, Francesco; Ley, Brett; Wolters, Paul J; King, Talmadge E; Collard, Harold R; Lee, Joyce S

    2017-11-01

    Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course. We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index. Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years. The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

  4. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

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    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  5. Cardiovascular disease risk score prediction models for women and its applicability to Asians

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    Goh LGH

    2014-03-01

    Full Text Available Louise GH Goh,1 Satvinder S Dhaliwal,1 Timothy A Welborn,2 Peter L Thompson,2–4 Bruce R Maycock,1 Deborah A Kerr,1 Andy H Lee,1 Dean Bertolatti,1 Karin M Clark,1 Rakhshanda Naheed,1 Ranil Coorey,1 Phillip R Della5 1School of Public Health, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia; 2Sir Charles Gairdner Hospital, Nedlands, Perth, WA, Australia; 3School of Population Health, University of Western Australia, Perth, WA, Australia; 4Harry Perkins Institute for Medical Research, Perth, WA, Australia; 5School of Nursing and Midwifery, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia Purpose: Although elevated cardiovascular disease (CVD risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Methods: Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20–69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Results: Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were

  6. Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians.

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    Kanjilal, S; Rao, V S; Mukherjee, M; Natesha, B K; Renuka, K S; Sibi, K; Iyengar, S S; Kakkar, Vijay V

    2008-01-01

    The increasing pressure on health resources has led to the emergence of risk assessment as an essential tool in the management of cardiovascular disease (CVD). Concern exists regarding the validity of their generalization to all populations. Existing risk scoring models do not incorporate emerging 'novel' risk factors. In this context, the aim of the study was to examine the relevance of British, European, and Framingham predictive CVD risk scores to the asymptomatic high risk Indian population. Blood samples drawn from the participants were analyzed for various 'traditional' and 'novel' biomarkers, and their CVD risk factor profiling was also done. The Framingham model defined only 5% of the study cohort to be at high risk, which appears to be an underestimation of CVD risk in this genetically predisposed population. These subjects at high risk had significantly elevated levels of lipid, pro-inflammatory, pro-thrombotic, and serological markers. It is more relevant to develop risk predictive scores for application to the Indian population. This study substantiates the argument that alternative approaches to risk stratification are required in order to make them more adaptable and applicable to different populations with varying risk factor and disease patterns.

  7. A summary risk score for the prediction of Alzheimer disease in elderly persons.

    Science.gov (United States)

    Reitz, Christiane; Tang, Ming-Xin; Schupf, Nicole; Manly, Jennifer J; Mayeux, Richard; Luchsinger, José A

    2010-07-01

    To develop a simple summary risk score for the prediction of Alzheimer disease in elderly persons based on their vascular risk profiles. A longitudinal, community-based study. New York, New York. Patients One thousand fifty-one Medicare recipients aged 65 years or older and residing in New York who were free of dementia or cognitive impairment at baseline. We separately explored the associations of several vascular risk factors with late-onset Alzheimer disease (LOAD) using Cox proportional hazards models to identify factors that would contribute to the risk score. Then we estimated the score values of each factor based on their beta coefficients and created the LOAD vascular risk score by summing these individual scores. Risk factors contributing to the risk score were age, sex, education, ethnicity, APOE epsilon4 genotype, history of diabetes, hypertension or smoking, high-density lipoprotein levels, and waist to hip ratio. The resulting risk score predicted dementia well. According to the vascular risk score quintiles, the risk to develop probable LOAD was 1.0 for persons with a score of 0 to 14 and increased 3.7-fold for persons with a score of 15 to 18, 3.6-fold for persons with a score of 19 to 22, 12.6-fold for persons with a score of 23 to 28, and 20.5-fold for persons with a score higher than 28. While additional studies in other populations are needed to validate and further develop the score, our study suggests that this vascular risk score could be a valuable tool to identify elderly individuals who might be at risk of LOAD. This risk score could be used to identify persons at risk of LOAD, but can also be used to adjust for confounders in epidemiologic studies.

  8. A risk prediction score for invasive mold disease in patients with hematological malignancies.

    Directory of Open Access Journals (Sweden)

    Marta Stanzani

    Full Text Available BACKGROUND: A risk score for invasive mold disease (IMD in patients with hematological malignancies could facilitate patient screening and improve the targeted use of antifungal prophylaxis. METHODS: We retrospectively analyzed 1,709 hospital admissions of 840 patients with hematological malignancies (2005-2008 to collect data on 17 epidemiological and treatment-related risk factors for IMD. Multivariate regression was used to develop a weighted risk score based on independent risk factors associated with proven or probable IMD, which was prospectively validated during 1,746 hospital admissions of 855 patients from 2009-2012. RESULTS: Of the 17 candidate variables analyzed, 11 correlated with IMD by univariate analysis, but only 4 risk factors (neutropenia, lymphocytopenia or lymphocyte dysfunction in allogeneic hematopoietic stem cell transplant recipients, malignancy status, and prior IMD were retained in the final multivariate model, resulting in a weighted risk score 0-13. A risk score of 5% of IMD, with a negative predictive value (NPV of 0.99, (95% CI 0.98-0.99. During 2009-2012, patients with a calculated risk score at admission of 6 (0.9% vs. 10.6%, P <0.001. CONCLUSION: An objective, weighted risk score for IMD can accurately discriminate patients with hematological malignancies at low risk for developing mold disease, and could possibly facilitate "screening-out" of low risk patients less likely to benefit from intensive diagnostic monitoring or mold-directed antifungal prophylaxis.

  9. Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

    Science.gov (United States)

    Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

    2015-01-01

    This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

  10. Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

    Directory of Open Access Journals (Sweden)

    Tin Tin Su

    2015-01-01

    Full Text Available Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

  11. Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

    Science.gov (United States)

    Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

    2018-01-01

    Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate

  12. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries

    DEFF Research Database (Denmark)

    Ueda, Peter; Woodward, Mark; Lu, Yuan

    2017-01-01

    BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and ...

  13. Developing and evaluating polygenic risk prediction models for stratified disease prevention.

    Science.gov (United States)

    Chatterjee, Nilanjan; Shi, Jianxin; García-Closas, Montserrat

    2016-07-01

    Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

  14. Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects.

    Directory of Open Access Journals (Sweden)

    Jane Maryam Rondina

    2014-12-01

    Full Text Available Recent literature has presented evidence that cardiovascular risk factors (CVRF play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies in a sample of healthy elderly individuals. We aim to answer the following questions: Is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: i we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease. ii When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. iii We found important gender differences, and the possible causes of that finding are discussed.

  15. Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: a retrospective cohort study.

    Science.gov (United States)

    Rubin, Daniel J; Golden, Sherita Hill; McDonnell, Marie E; Zhao, Huaqing

    2017-08-01

    To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™). A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample. Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles). The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Predictive risk factors for chronic low back pain in Parkinson's disease.

    Science.gov (United States)

    Ozturk, Erhan Arif; Kocer, Bilge Gonenli

    2018-01-01

    Although previous studies have reported that the prevalence of low back pain in Parkinson's disease was over 50% and low back pain was often classified as chronic, risk factors of chronic low back pain have not been previously investigated. The aim of this study was to determine the predictive risk factors of chronic low back pain in Parkinson's disease. One hundred and sixty-eight patients with Parkinson's disease and 179 controls were consecutively included in the study. Demographic data of the two groups and disease characteristics of Parkinson's disease patient group were recorded. Low back pain lasting for ≥3 months was evaluated as chronic. Firstly, the bivariate correlations were calculated between chronic low back pain and all possible risk factors. Then, a multivariate regression was used to evaluate the impact of the predictors of chronic low back pain. The frequency of chronic low back pain in Parkinson's disease patients and controls were 48.2% and 26.7%, respectively (p chronic low back pain in Parkinson's disease were general factors including age (odds ratio = 1.053, p = 0.032) and Hospital Anxiety and Depression Scale - Depression subscore (odds ratio = 1.218, p = 0.001), and Parkinson's disease-related factors including rigidity (odds ratio = 5.109, p = 0.002) and posture item scores (odds ratio = 5.019, p = 0.0001). The chronic low back pain affects approximately half of the patients with Parkinson's disease. Prevention of depression or treatment recommendations for managing depression, close monitoring of anti- parkinsonian medication to keep motor symptoms under control, and attempts to prevent, correct or reduce abnormal posture may help reduce the frequency of chronic low back pain in Parkinson's disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease.

    Science.gov (United States)

    Noyce, Alastair J; R'Bibo, Lea; Peress, Luisa; Bestwick, Jonathan P; Adams-Carr, Kerala L; Mencacci, Niccolo E; Hawkes, Christopher H; Masters, Joseph M; Wood, Nicholas; Hardy, John; Giovannoni, Gavin; Lees, Andrew J; Schrag, Anette

    2017-02-01

    A number of early features can precede the diagnosis of Parkinson's disease (PD). To test an online, evidence-based algorithm to identify risk indicators of PD in the UK population. Participants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed. A total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores. The online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder

  18. Prediction of cardiovascular disease risk among low-income urban dwellers in metropolitan Kuala Lumpur, Malaysia.

    Science.gov (United States)

    Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Bulgiba, Awang; Majid, Hazreen Abdul

    2015-01-01

    We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

  19. Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia

    Directory of Open Access Journals (Sweden)

    Tin Tin Su

    2015-01-01

    Full Text Available We aimed to predict the ten-year cardiovascular disease (CVD risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM. Altogether 882 adults (≥30 years old with no CVD history were randomly selected. The classic FRS model (figures in parentheses are from the modified model revealed that 20.5% (21.8% and 38.46% (38.9% of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

  20. Predicting coronary heart disease

    DEFF Research Database (Denmark)

    Sillesen, Henrik; Fuster, Valentin

    2012-01-01

    Atherosclerosis is the leading cause of death and disabling disease. Whereas risk factors are well known and constitute therapeutic targets, they are not useful for prediction of risk of future myocardial infarction, stroke, or death. Therefore, methods to identify atherosclerosis itself have bee...

  1. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

    Science.gov (United States)

    Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

    2016-11-08

    The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

  2. The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

    Science.gov (United States)

    Cameron, Adrian

    2010-02-01

    The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

  3. Comparison of three lifecourse models of poverty in predicting cardiovascular disease risk in youth.

    Science.gov (United States)

    Kakinami, Lisa; Séguin, Louise; Lambert, Marie; Gauvin, Lise; Nikiema, Béatrice; Paradis, Gilles

    2013-08-01

    Childhood poverty heightens the risk of adulthood cardiovascular disease (CVD), but the underlying pathways are poorly understood. Three lifecourse models have been proposed but have never been tested among youth. We assessed the longitudinal association of childhood poverty with CVD risk factors in 10-year-old youth according to the timing, accumulation, and mobility models. The Québec Longitudinal Study of Child Development birth cohort was established in 1998 (n = 2120). Poverty was defined as annual income below the low-income thresholds defined by Statistics Canada. Multiple imputation was used for missing data. Multivariable linear regression models adjusted for gender, pubertal stage, parental education, maternal age, whether the household was a single parent household, whether the child was overweight or obese, the child's physical activity in the past week, and family history. Approximately 40% experienced poverty at least once, 16% throughout childhood, and 25% intermittently. Poverty was associated with significantly elevated triglycerides and insulin according to the timing and accumulation models, although the timing model was superior for predicting insulin and the accumulation model was superior for predicting triglycerides. Early and prolonged exposure to poverty significantly increases CVD risk among 10-year-old youth. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Changes in predicted protein disorder tendency may contribute to disease risk

    Directory of Open Access Journals (Sweden)

    Hu Yang

    2011-12-01

    Full Text Available Abstract Background Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results Using the exonic single nucleotide variations (SNVs identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

  5. An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons

    DEFF Research Database (Denmark)

    Friis-Møller, Nina; Ryom, Lene; Smith, Colette

    2016-01-01

    ,663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance...... significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p models also more accurately predicted five-year CVD-risk for key prognostic subgroups...... to quantify risk and to guide preventive care....

  6. Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

    Science.gov (United States)

    Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

    2016-11-01

    The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

    Science.gov (United States)

    Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

    2015-08-01

    Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

    DEFF Research Database (Denmark)

    Pena, Michelle J; Jankowski, Joachim; Heinze, Georg

    2015-01-01

    OBJECTIVE: Micro and macroalbuminuria are strong risk factors for progression of nephropathy in patients with hypertension or type 2 diabetes. Early detection of progression to micro and macroalbuminuria may facilitate prevention and treatment of renal diseases. We aimed to develop plasma...... proteomics classifiers to predict the development of micro or macroalbuminuria in hypertension or type 2 diabetes. METHODS: Patients with hypertension (n = 125) and type 2 diabetes (n = 82) were selected for this case-control study from the Prevention of REnal and Vascular ENd-stage Disease cohort....... RESULTS: In hypertensive patients, the classifier improved risk prediction for transition in albuminuria stage on top of the reference model (C-index from 0.69 to 0.78; P diabetes, the classifier improved risk prediction for transition from micro to macroalbuminuria (C-index from 0...

  9. Cardiovascular disease (CVD) and chronic kidney disease (CKD) event rates in HIV-positive persons at high predicted CVD and CKD risk

    DEFF Research Database (Denmark)

    Boyd, Mark A; Mocroft, Amanda; Ryom, Lene

    2017-01-01

    BACKGROUND: The Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study has developed predictive risk scores for cardiovascular disease (CVD) and chronic kidney disease (CKD, defined as confirmed estimated glomerular filtration rate [eGFR] ≤ 60 ml/min/1.73 m2) events in HIV...

  10. Heart disease - risk factors

    Science.gov (United States)

    Heart disease - prevention; CVD - risk factors; Cardiovascular disease - risk factors; Coronary artery disease - risk factors; CAD - risk ... a certain health condition. Some risk factors for heart disease you cannot change, but some you can. ...

  11. Models to Predict the Burden of Cardiovascular Disease Risk in a Rural Mountainous Region of Vietnam

    NARCIS (Netherlands)

    Nguyen, Thi Phuong Lan; Schuiling-Veninga, Nynke; Nguyen, Thi Bach Yen; Hang, Vu Thi Thu; Wright, E. Pamela; Postma, M.J.

    2014-01-01

    Objective: To compare and identify the most appropriate model to predict cardiovascular disease (CVD) in a rural area in Northern Vietnam, using data on hypertension from the communities. Methods: A cross-sectional survey was conducted including all residents in selected communities, aged 34 to 65

  12. Identifying Risk Factors for the Prediction of Hospital Readmission among Older Persons with Cardiovascular Disease.

    Science.gov (United States)

    Middleton, Renee Annette

    Older persons (55 years and older) with cardiovascular disease are at increased risk for hospital readmission when compared to other subgroups of our population. This issue presents an economic problem, a concern for the quality and type of care provided, and an urgent need to implement innovative strategies designed to reduce the rising cost of…

  13. Can dental pulp calcification predict the risk of ischemic cardiovascular disease?

    Science.gov (United States)

    Khojastepour, Leila; Bronoosh, Pegah; Khosropanah, Shahdad; Rahimi, Elham

    2013-09-01

    To report the association of pulp calcification with that of cardiovascular disease (CVD) using digital panoramic dental radiographs. Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specificity, positive predictive value and negative predictive value of panoramic radiography in predicting CVD were calculated. Out of 122 patients who met the criteria, 68.2% of the patients with CVD had pulp chamber calcifications. Pulp calcification in panoramic radiography had a sensitivity of 68.9% to predict CVD. This study demonstrates that patients with CVD show an increased incidence of pulp calcification compared with healthy patients. The findings suggest that pulp calcification on panoramic radiography may have possibilities for use in CVD screening.

  14. Natriuretic peptides: prediction of cardiovascular disease in the general population and high risk populations

    DEFF Research Database (Denmark)

    Hildebrandt, Per

    2009-01-01

    (General Practitioner) setting as in the acute setting. Supporting this use is a very strong prognostic value of the natriuretic peptides. This has been shown in as well heart failure as acute coronary syndromes, but also in the general population and in high-risk groups as patients with diabetes......, hypertension and coronary artery disease. This has of course raised interest for the use of the natriuretic peptides as a risk marker and for screening for heart failure with reduced systolic function in these populations. In symptomatic persons and in high risk populations, the natriuretic peptides have...... demonstrated a high sensitivity for ruling out the disease, if the right decision limits are choosen. Thus the number of normal echocardiographies can be reduced. More recently, the use in screening asymptomatic persons for left ventricular systolic dysfunction has gained more interest. In the unselected...

  15. Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine.

    Science.gov (United States)

    Vistisen, Dorte; Andersen, Gregers Stig; Hansen, Christian Stevns; Hulman, Adam; Henriksen, Jan Erik; Bech-Nielsen, Henning; Jørgensen, Marit Eika

    2016-03-15

    Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts. This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting. © 2016 American Heart Association, Inc.

  16. Development of a Coronary Heart Disease Risk Prediction Model for Type 1 Diabetes: The Pittsburgh CHD in Type 1 Diabetes Risk Mode

    NARCIS (Netherlands)

    Zgibor, J.C.; Ruppert, K.; Orchard, T.J.; Soedamah-Muthu, S.S.; Fuller, J.H.; Chaturvedi, N.; Roberts, M.S.

    2010-01-01

    Aim - To create a coronary heart disease (CHD) risk prediction model specific to type 1 diabetes. Methods - Development of the model used data from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC). EDC subjects had type 1 diabetes diagnosed between 1950 and 1980, received their

  17. Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function.

    Science.gov (United States)

    Xiao, Ena; Chen, Qiang; Goldman, Aaron L; Tan, Hao Yang; Healy, Kaitlin; Zoltick, Brad; Das, Saumitra; Kolachana, Bhaskar; Callicott, Joseph H; Dickinson, Dwight; Berman, Karen F; Weinberger, Daniel R; Mattay, Venkata S

    2017-11-01

    We explored the cumulative effect of several late-onset Alzheimer's disease (LOAD) risk loci using a polygenic risk profile score (RPS) approach on measures of hippocampal function, cognition, and brain morphometry. In a sample of 231 healthy control subjects (19-55 years of age), we used an RPS to study the effect of several LOAD risk loci reported in a recent meta-analysis on hippocampal function (determined by its engagement with blood oxygen level-dependent functional magnetic resonance imaging during episodic memory) and several cognitive metrics. We also studied effects on brain morphometry in an overlapping sample of 280 subjects. There was almost no significant association of LOAD-RPS with cognitive or morphometric measures. However, there was a significant negative relationship between LOAD-RPS and hippocampal function (familywise error [small volume correction-hippocampal region of interest] p risk score based on APOE haplotype, and for a combined LOAD-RPS + APOE haplotype risk profile score (p risk genes on hippocampal function even in healthy volunteers. The effect of LOAD-RPS on hippocampal function in the relative absence of any effect on cognitive and morphometric measures is consistent with the reported temporal characteristics of LOAD biomarkers with the earlier manifestation of synaptic dysfunction before morphometric and cognitive changes. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

  18. Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies.

    Directory of Open Access Journals (Sweden)

    Florian Mittag

    Full Text Available Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS. However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM as they allow for better subsequent interpretation without significant loss of accuracy.

  19. Can Dental Pulp Calcification Predict the Risk of Ischemic Cardiovascular Disease?

    Directory of Open Access Journals (Sweden)

    Leila Khojastepour

    2013-01-01

    Full Text Available Objective: To report the association of pulp calcification with that of cardiovascular disease (CVD using digital panoramic dental radiographs.Materials and Methods: Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specificity, positive predictive value and negative predictive value of panoramic radiography in predicting CVD were calculated.Results: Out of 122 patients who met the criteria, 68.2% of the patients with CVD had pulp chamber calcifications. Pulp calcification in panoramic radiography had a sensitivity of 68.9% to predict CVD.Conclusion: This study demonstrates that patients with CVD show an increased incidence of pulp calcification compared with healthy patients. The findings suggest that pulp calcification on panoramic radiography may have possibilities for use in CVD screening.

  20. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

    Science.gov (United States)

    Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

    2015-07-01

    As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  1. Cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Graversen, Peter; Abildstrøm, Steen Z.; Jespersen, Lasse

    2016-01-01

    Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential...

  2. Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.

    Science.gov (United States)

    Gomar, Jesus J; Bobes-Bascaran, Maria T; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

    2011-09-01

    Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic

  3. Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

    Directory of Open Access Journals (Sweden)

    Benjamin A Goldstein

    2014-08-01

    Full Text Available Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials & Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD in the Atherosclerosis Risk in Communities (ARIC cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor SNPs are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

  4. Quantifying prognosis with risk predictions.

    Science.gov (United States)

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  5. Circulating biomarkers for predicting cardiovascular disease risk; a systematic review and comprehensive overview of meta-analyses.

    Directory of Open Access Journals (Sweden)

    Thijs C van Holten

    Full Text Available BACKGROUND: Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming number of studies and meta-analyses on biomarkers and cardiovascular disease, there are no comprehensive studies comparing the relevance of each biomarker. We performed a systematic review of meta-analyses on levels of serological biomarkers for atherothrombosis to compare the relevance of the most commonly studied biomarkers. METHODS AND FINDINGS: Medline and Embase were screened on search terms that were related to "arterial ischemic events" and "meta-analyses". The meta-analyses were sorted by patient groups without pre-existing cardiovascular disease, with cardiovascular disease and heterogeneous groups concerning general populations, groups with and without cardiovascular disease, or miscellaneous. These were subsequently sorted by end-point for cardiovascular disease or stroke and summarized in tables. We have identified 85 relevant full text articles, with 214 meta-analyses. Markers for primary cardiovascular events include, from high to low result: C-reactive protein, fibrinogen, cholesterol, apolipoprotein B, the apolipoprotein A/apolipoprotein B ratio, high density lipoprotein, and vitamin D. Markers for secondary cardiovascular events include, from high to low result: cardiac troponins I and T, C-reactive protein, serum creatinine, and cystatin C. For primary stroke, fibrinogen and serum uric acid are strong risk markers. Limitations reside in that there is no acknowledged search strategy for prognostic studies or meta-analyses. CONCLUSIONS: For primary cardiovascular events, markers with strong predictive potential are mainly associated with lipids. For secondary cardiovascular events, markers are more associated with ischemia. Fibrinogen is a

  6. Molecular determination of RHD zygosity: predicting risk of hemolytic disease of the fetus and newborn related to anti-D.

    Science.gov (United States)

    Pirelli, Kevin J; Pietz, Bradley C; Johnson, Susan T; Pinder, Holly L; Bellissimo, Daniel B

    2010-12-01

    Development of an accurate molecular method for paternal RHD zygosity to predict risk to a fetus for hemolytic disease of the fetus and newborn (HDFN) related to anti-D. Quantitative fluorescence polymerase chain reaction (QF-PCR) was used to detect RHD exons 5 and 7, using RHCE exon 7 as an internal control. The genotype and zygosity were determined from the peak area ratios of RHD exon 5 or 7 to RHCE exon 7. We tested 25 Caucasian and 25 African American (AA) samples whose zygosity was predicted from the Rh phenotype and an alternate molecular method. In addition, we tested 71 paternal samples from prenatal cases where fetal testing was performed. RHD/RHCE ratios clearly distinguished the RHD/D and RHD/d genotypes. RHD variants were recognized when RHD exon 5 copy number was discordant with exon 7. The molecular assay identified eight cases where the phenotype incorrectly assigned zygosity and we observed three false-negatives in the hybrid Rhesus box assay. The prenatal results were consistent with the zygosity determined for the paternal samples in our study. This QF-PCR method accurately determines RHD zygosity in Caucasians and AAs and will help predict the risk that a fetus will inherit RHD. Copyright © 2010 John Wiley & Sons, Ltd.

  7. Standard deviation of carotid young's modulus and presence or absence of plaque improves prediction of coronary heart disease risk.

    Science.gov (United States)

    Niu, Lili; Zhang, Yanling; Qian, Ming; Xiao, Yang; Meng, Long; Zheng, Rongqin; Zheng, Hairong

    2017-11-01

    The stiffness of large arteries and the presence or absence of plaque are associated with coronary heart disease (CHD). Because arterial walls are biologically heterogeneous, the standard deviation of Young's modulus (YM-std) of the large arteries may better predict coronary atherosclerosis. However, the role of YM-std in the occurrence of coronary events has not been addressed so far. Therefore, this study investigated whether the carotid YM-std and the presence or absence of plaque improved CHD risk prediction. One hundred and three patients with CHD (age 66 ± 11 years) and 107 patients at high risk of atherosclerosis (age 61 ± 7 years) were recruited. Carotid YM was measured by the vessel texture matching method, and YM-std was calculated. Carotid intima-media thickness was measured by the MyLab 90 ultrasound Platform employed dedicated software RF-tracking technology. In logistic regression analysis, YM-std (OR = 1·010; 95% CI = 1·003-1·016), carotid plaque (OR = 16·759; 95% CI = 3·719-75·533) and YM-std plus plaque (OR = 0·989; 95% CI = 0·981-0·997) were independent predictors of CHD. The traditional risk factors (TRF) plus YM-std plus plaque model showed a significant improvement in area under the receiver-operating characteristic curve (AUC), which increased from 0·717 (TRF only) to 0·777 (95% CI for the difference in adjusted AUC: 0·010-0·110). Carotid YM-std is a powerful independent predictor of CHD. Adding plaque and YM-std to TRF improves CHD risk prediction. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

  8. Meta-Prediction of the Effect of Methylenetetrahydrofolate Reductase Polymorphisms and Air Pollution on Alzheimer’s Disease Risk

    Directory of Open Access Journals (Sweden)

    Suh-Mian Wu

    2017-01-01

    Full Text Available Background: Alzheimer’s disease (AD is a significant public health issue. AD has been linked with methylenetetrahydrofolate reductase (MTHFR C677T polymorphism, but the findings have been inconsistent. The purpose of this meta-predictive analysis is to examine the associations between MTHFR polymorphisms and epigenetic factors, including air pollution, with AD risk using big data analytics approaches. Methods and Results: Forty-three studies (44 groups were identified by searching various databases. MTHFR C677T TT and CT genotypes had significant associations with AD risk in all racial populations (RR = 1.13, p = 0.0047; and RR = 1.12, p < 0.0001 respectively. Meta-predictive analysis showed significant increases of percentages of MTHFR C677T polymorphism with increased air pollution levels in both AD case group and control group (p = 0.0021–0.0457; with higher percentages of TT and CT genotypes in the AD case group than that in the control group with increased air pollution levels. Conclusions: The impact of MTHFR C677T polymorphism on susceptibility to AD was modified by level of air pollution. Future studies are needed to further examine the effects of gene-environment interactions including air pollution on AD risk for world populations.

  9. A risk score for predicting coronary artery disease in women with angina pectoris and abnormal stress test finding.

    Science.gov (United States)

    Lo, Monica Y; Bonthala, Nirupama; Holper, Elizabeth M; Banks, Kamakki; Murphy, Sabina A; McGuire, Darren K; de Lemos, James A; Khera, Amit

    2013-03-15

    Women with angina pectoris and abnormal stress test findings commonly have no epicardial coronary artery disease (CAD) at catheterization. The aim of the present study was to develop a risk score to predict obstructive CAD in such patients. Data were analyzed from 337 consecutive women with angina pectoris and abnormal stress test findings who underwent cardiac catheterization at our center from 2003 to 2007. Forward selection multivariate logistic regression analysis was used to identify the independent predictors of CAD, defined by ≥50% diameter stenosis in ≥1 epicardial coronary artery. The independent predictors included age ≥55 years (odds ratio 2.3, 95% confidence interval 1.3 to 4.0), body mass index stress imaging (odds ratio 2.8, 95% confidence interval 1.5 to 5.5), and exercise capacity statistic of 0.745 (95% confidence interval 0.70 to 0.79), and an optimized cutpoint of a score of ≤2 included 62% of the subjects and had a negative predictive value of 80%. In conclusion, a simple clinical risk score of 7 characteristics can help differentiate those more or less likely to have CAD among women with angina pectoris and abnormal stress test findings. This tool, if validated, could help to guide testing strategies in women with angina pectoris. Copyright © 2013 Elsevier Inc. All rights reserved.

  10. Meta-Prediction of the Effect of Methylenetetrahydrofolate Reductase Polymorphisms and Air Pollution on Alzheimer's Disease Risk.

    Science.gov (United States)

    Wu, Suh-Mian; Chen, Zhao-Feng; Young, Lufei; Shiao, S Pamela K

    2017-01-11

    Background : Alzheimer's disease (AD) is a significant public health issue. AD has been linked with methylenetetrahydrofolate reductase ( MTHFR ) C677T polymorphism, but the findings have been inconsistent. The purpose of this meta-predictive analysis is to examine the associations between MTHFR polymorphisms and epigenetic factors, including air pollution, with AD risk using big data analytics approaches. Methods and Results : Forty-three studies (44 groups) were identified by searching various databases. MTHFR C677T TT and CT genotypes had significant associations with AD risk in all racial populations (RR = 1.13, p = 0.0047; and RR = 1.12, p analysis showed significant increases of percentages of MTHFR C677T polymorphism with increased air pollution levels in both AD case group and control group ( p = 0.0021-0.0457); with higher percentages of TT and CT genotypes in the AD case group than that in the control group with increased air pollution levels. Conclusions : The impact of MTHFR C677T polymorphism on susceptibility to AD was modified by level of air pollution. Future studies are needed to further examine the effects of gene-environment interactions including air pollution on AD risk for world populations.

  11. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol.

    Science.gov (United States)

    Taljaard, Monica; Tuna, Meltem; Bennett, Carol; Perez, Richard; Rosella, Laura; Tu, Jack V; Sanmartin, Claudia; Hennessy, Deirdre; Tanuseputro, Peter; Lebenbaum, Michael; Manuel, Douglas G

    2014-10-23

    Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible

  12. Can Dental Pulp Calcification Predict the Risk of Ischemic Cardiovascular Disease?

    OpenAIRE

    Khojastepour, Leila; Bronoosh, Pegah; Khosropanah, Shahdad; Rahimi, Elham

    2013-01-01

    Objective: To report the association of pulp calcification with that of cardiovascular disease (CVD) using digital panoramic dental radiographs. Materials and Methods: Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specif...

  13. Electrocardiographic Changes Improve Risk Prediction in Asymptomatic Persons Age 65 Years or Above Without Cardiovascular Disease

    DEFF Research Database (Denmark)

    Jørgensen, Peter Godsk; Jensen, Jan S; Marott, Jacob L

    2014-01-01

    endpoint was fatal cardiovascular disease (CVD) event and the secondary was fatal or nonfatal CVD event. In our study, 2,236 fatal CVD and 3,849 fatal or nonfatal CVD events occurred during a median of 11.9 and 9.8 years of follow-up. RESULTS: ECG changes were frequently present (30.6%) and associated......: In all, 6,991 participants from the Copenhagen Heart Study attending an examination at age ≥65 years were included. ECG changes were defined as Q waves, ST-segment depression, T-wave changes, ventricular conduction defects, and left ventricular hypertrophy based on the Minnesota code. The primary...

  14. Predicting erectile dysfunction following surgical correction of Peyronie's disease without inflatable penile prosthesis placement: vascular assessment and preoperative risk factors.

    Science.gov (United States)

    Taylor, Frederick L; Abern, Michael R; Levine, Laurence A

    2012-01-01

    Surgical therapy remains the gold standard treatment for Peyronie's Disease (PD). Surgical options include plication, grafting, and placement of inflatable penile prosthesis (IPP). Postoperative erectile dysfunction (ED) is a potential complication for PD surgery without IPP. We present our large series follow-up to evaluate preoperative risk factors for postoperative ED. The aim of this study is to evaluate preoperative risk factors for the development of ED following surgical correction of PD taking into account the degree of curvature, graft size, surgical approach, hypertension, hyperlipidemia, diabetes, smoking history, preoperative use of phosphodiesterase 5 inhibitors (PDE5), and preoperative duplex ultrasound findings including peak systolic and end diastolic velocities and resistive index. We identified 218 men undergoing either tunica albuginea plication (TAP) or partial plaque excision with pericardial grafting for PD following a previously published algorithm between November 1992 and April 2007. Preoperative and postoperative erectile function, curvature characteristics, presence of vascular risk factors, and duplex ultrasound findings were available on 109 patients. Our primary outcome measure is the development of ED after surgery for PD. Ten percent of TAP and 21% of plaque excision with grafting patients developed postoperative ED. Neither curve direction (P = 0.76), graft area (P = 0.78), surgical approach (P = 0.12), chronic hypertension (P = 0.51), hyperlipidemia (P = 0.87), diabetes (P = 0.69), nor smoking history (P = 0.99) were significant predictors of postoperative ED. No combination of risk factors was found to be predictive of postoperative ED. Preoperative use of PDE5 was not a significant predictor of postoperative ED (P = 0.33). Neither peak systolic, end diastolic, nor resistive index were significant predictors of ED (P = 0.28, 0.28, and 0.25, respectively). This long-term follow-up of a large published series suggests that neither

  15. Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

    Science.gov (United States)

    Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

    2012-04-01

    Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

  16. Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership.

    Science.gov (United States)

    Wang, Qianqian; Ma, Yanyuan; Wang, Yuanjia

    2017-10-01

    Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to identify the covariates that have different effects or common effects in distinct populations, which enables parsimonious modeling and better understanding of the difference across populations. The methods are illustrated through extensive simulation studies and a real data example.

  17. Emotional intimacy predicts condom use: findings in a group at high sexually transmitted disease risk.

    Science.gov (United States)

    Damani, R; Ross, M W; Aral, S O; Berman, S; St Lawrence, J; Williams, M L

    2009-11-01

    Previous studies have reported an inverse relationship between condom use and emotional intimacy. The aim of this study was to determine the relationship between condom use and emotional intimacy. The study was a gonorrhoea case-comparison study with the samples being drawn from public health clinics (cases) and select bars/nightclubs (places) of Houston, TX (n = 215). Data were collected by questionnaires administered on a laptop computer. The majority of respondents were African-American (97.7%), women (69.3%) and had either high school or GED education (72.6%). Condom use with the last sexual partner was analysed along with intimacy with that partner assessed on a 3-point scale. Analysis showed that higher intimacy was related to greater condom use which was significant in men but not in women. In conclusion, these data were opposite to those of previous studies, which showed an inverse relationship between condom use and emotional intimacy. We hypothesize that in a high-risk environment, people exert more effort in protecting those they feel closer to. These data suggest a need to further explore the complex relationship between emotional intimacy and condom use.

  18. The Myocardial Perfusion Scintigraphy in Predicting Risk for Coronary Artery Disease in Patients with Anxiety and Depression Symptoms

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    Billur Çalışkan

    2016-06-01

    Full Text Available INTRODUCTION: An association between psychological factors and cardiovascular disease, has long been suspected. However it is not clear whether chest pain is caused by emotional distress or whether coronary spasms are caused by the onset of coronary artery disease (CAD. We aimed to predict the risk for CAD in patients referred to myocardial perfusion imaging (MPI with chest pain using depression, stress, and anxiety symptoms. METHODS: The emotional status of all patients was evaluated using the Hospital Anxiety and Depression Scale (HADS-A and HADS-D, the State and Trait Anxiety Inventory (STAI-1 and STAI-2, the Perceived Stress Scale (PSS, and the Anxiety Sensitivity Index-3 (ASI. Myocardial perfusion was measured using a 17-segment model and 5-point scoring system (0: normal perfusion; 4: no perfusion. RESULTS: MPI revealed reversible perfusion defects in 24 of 141 patients and no perfusion defects in 117 patients. The STAI-2 and HADS-A and HADS-D scores were significantly higher in patients with myocardial ischemia than in those without (STAI-2: 50.8 ± 7.5 vs. 46.3 ± 7.1, respectively; p = 0.008; HADS-A: 9.5 ± 3.9 vs. 7.8 ± 3.4, respectively; p = 0.033; HADS-D: 8.7 ± 3.0 vs. 7.3 ± 3.0, respectively; p = 0.05. Unadjusted correlation analysis showed that there was statistically significant correlation between reversible perfusion defects and anxiety scores (r=0.186, p= 0.029. DISCUSSION AND CONCLUSION: The patients with symptoms of depression and high-trait anxiety may be at higher risk of myocardial ischemia than patients without such symptoms. Thus, the emotional status of patients should be taken into consideration during clinical evaluation for CAD.

  19. The Clinical Features and Predictive Risk Factors for Reoperation in Patients With Perianal Crohn Diseases; A Multi-Center Study of a Korean Inflammatory Bowel Disease Study Group

    Science.gov (United States)

    Lee, Jae Bum; Yoon, Seo-Gue; Park, Kyu Joo; Lee, Kang Young; Kim, Dae Dong; Yoon, Sang Nam

    2015-01-01

    Purpose Perianal lesions are common in Crohn disease, but their clinical course is unpredictable. Nevertheless, predicting the clinical course after surgery for perianal Crohn disease (PCD) is important because repeated operations may decrease patient's quality of life. The aim of this study was to predict the risk of reoperation in patients with PCD. Methods From September 1994 to February 2010, 377 patients with PCD were recruited in twelve major tertiary university-affiliated hospitals and two specialized colorectal hospitals in Korea. Data on the patient's demographics, clinical features, and surgical outcomes were analyzed. Results Among 377 patients, 227 patients were ultimately included in the study. Among the 227 patients, 64 patients underwent at least one reoperation. The median period of reoperation following the first perianal surgery was 94 months. Overall 3-year, 5-year, and 10-year cumulative rates of reoperation-free individuals were 68.8%, 61.2%, and 50.5%, respectively. In multivariate analysis (Cox-regression hazard model), reoperation was significantly correlated with an age of onset less than 20 years (hazard ratio [HR], 1.93; 95% confidence interval [CI], 1.07-3.48; P = 0.03), history of abdominal surgery (HR, 1.99; 95% CI, 1.08-3.64; P = 0.03), and the type of surgery. Among types of surgery, fistulotomy or fistulectomy was associated with a decreased incidence of reoperation in comparison with incision and drainage (HR, 0.19; 95% CI, 0.09-0.42; P < 0.001). Conclusion Young age of onset and a history of abdominal surgery were associated with a high risk of reoperation for PCD, and the risk of reoperation were relatively low in fistulotomy or fistulectomy procedures. PMID:26576395

  20. Long-term predictive value of postsurgical cortisol concentrations for cure and risk of recurrence in Cushing's disease

    NARCIS (Netherlands)

    Pereira, Alberto M.; van Aken, Maarten O.; van Dulken, Hans; Schutte, Pieter J.; Biermasz, Nienke R.; Smit, Jan W. A.; Roelfsema, Ferdinand; Romijn, Johannes A.

    2003-01-01

    We assessed the value of postoperative plasma cortisol concentrations to predict cure and recurrence of Cushing's disease after transsphenoidal surgery (TS). Seventy-eight of 80 consecutive patients treated by TS for Cushing's disease were evaluated. TS cured 72% (n = 56) of the patients. Two weeks

  1. Usefulness of Genetic Polymorphisms and Conventional Risk Factors to Predict Coronary Heart Disease in Patients With Familial Hypercholesterolemia

    NARCIS (Netherlands)

    van der Net, Jeroen B.; Janssens, A. Cecile J. W.; Defesche, Joep C.; Kastelein, John J. P.; Sijbrands, Eric J. G.; Steyerberg, Ewout W.

    2009-01-01

    Familial hypercholesterolemia (FH) is an autosomal dominant disorder with an associated high risk of coronary heart disease (CHD). The considerable variation in age of onset of CHD in patients with FH is believed to arise from conventional risk factors, as well as genetic variation other than in the

  2. A coronary heart disease risk model for predicting the effect of potent antiretroviral therapy in HIV-1 infected men

    DEFF Research Database (Denmark)

    May, Margaret; Sterne, Jonathan A C; Shipley, Martin

    2007-01-01

    Many HIV-infected patients on highly active antiretroviral therapy (HAART) experience metabolic complications including dyslipidaemia and insulin resistance, which may increase their coronary heart disease (CHD) risk. We developed a prognostic model for CHD tailored to the changes in risk factors...

  3. Circulating biomarkers for predicting cardiovascular disease risk : a systematic review and comprehensive overview of meta-analyses

    NARCIS (Netherlands)

    Holten, van T.C.; Waanders, L.F.; Groot, de P.G.; Vissers, J.; Hoefer, I.E.; Pasterkamp, G.; Prins, M.W.J.; Roest, M.

    2013-01-01

    Background : Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming

  4. Cardiovascular risk prediction in the Netherlands

    NARCIS (Netherlands)

    Dis, van S.J.

    2011-01-01

    Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These

  5. Heterogeneous associations between smoking and a wide range of initial presentations of cardiovascular disease in 1937360 people in England: lifetime risks and implications for risk prediction.

    Science.gov (United States)

    Pujades-Rodriguez, Mar; George, Julie; Shah, Anoop Dinesh; Rapsomaniki, Eleni; Denaxas, Spiros; West, Robert; Smeeth, Liam; Timmis, Adam; Hemingway, Harry

    2015-02-01

    It is not known how smoking affects the initial presentation of a wide range of chronic and acute cardiovascular diseases (CVDs), nor the extent to which associations are heterogeneous. We estimated the lifetime cumulative incidence of 12 CVD presentations, and examined associations with smoking and smoking cessation. Cohort study of 1.93 million people aged ≥30years, with no history of CVD, in 1997-2010. Individuals were drawn from linked electronic health records in England, covering primary care, hospitalizations, myocardial infarction (MI) registry and cause-specific mortality (the CALIBER programme). During 11.6 million person-years of follow-up, 114859 people had an initial non-fatal or fatal CVD presentation. By age 90 years, current vs never smokers' lifetime risks varied from 0.4% vs 0.2% for subarachnoid haemorrhage (SAH), to 8.9% vs 2.6% for peripheral arterial disease (PAD). Current smoking showed no association with cardiac arrest or sudden cardiac death [hazard ratio (HR)=1.04, 95% confidence interval (CI) 0.91-1.19).The strength of association differed markedly according to disease type: stable angina (HR=1.08, 95% CI 1.01-1.15),transient ischaemic attack (HR=1.41, 95% CI 1.28-1.55), unstable angina (HR=1.54, 95% CI 1.38-1.72), intracerebral haemorrhage (HR=1.61, 95% CI 1.37-1.89), heart failure (HR=1.62, 95% CI 1.47-1.79), ischaemic stroke (HR=1.90, 95% CI 1.72-2.10), MI (HR=2.32, 95% CI 2.20-2.45), SAH (HR= 2.70, 95% CI 2.27-3.21), PAD (HR=5.16, 95% CI 4.80-5.54) and abdominal aortic aneurysm (AAA) (HR=5.18, 95% CI 4.61-5.82). Population-attributable fractions were lower for women than men for unheralded coronary death, ischaemic stroke, PAD and AAA. Ten years after quitting smoking, the risks of PAD, AAA (in men) and unheralded coronary death remained increased (HR=1.36, 1.47 and 2.74, respectively). The heterogeneous associations of smoking with different CVD presentations suggests different underlying mechanisms and have important

  6. Women's Heart Disease: Heart Disease Risk Factors

    Science.gov (United States)

    ... this page please turn JavaScript on. Feature: Women's Heart Disease Heart Disease Risk Factors Past Issues / Winter 2014 Table ... or habits may raise your risk for coronary heart disease (CHD). These conditions are known as risk ...

  7. Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

    NARCIS (Netherlands)

    Pena, Michelle J.; Jankowski, Joachim; Heinze, Georg; Kohl, Maria; Heinzel, Andreas; Bakker, Stephan J. L.; Gansevoort, Ron T.; Rossing, Peter; de Zeeuw, Dick; Heerspink, Hiddo J. Lambers; Jankowski, Vera

    2015-01-01

    OBJECTIVE: Micro and macroalbuminuria are strong risk factors for progression of nephropathy in patients with hypertension or type 2 diabetes. Early detection of progression to micro and macroalbuminuria may facilitate prevention and treatment of renal diseases. We aimed to develop plasma proteomics

  8. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

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    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  9. Cardiovascular disease (CVD and chronic kidney disease (CKD event rates in HIV-positive persons at high predicted CVD and CKD risk: A prospective analysis of the D:A:D observational study.

    Directory of Open Access Journals (Sweden)

    Mark A Boyd

    2017-11-01

    Full Text Available The Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D study has developed predictive risk scores for cardiovascular disease (CVD and chronic kidney disease (CKD, defined as confirmed estimated glomerular filtration rate [eGFR] ≤ 60 ml/min/1.73 m2 events in HIV-positive people. We hypothesized that participants in D:A:D at high (>5% predicted risk for both CVD and CKD would be at even greater risk for CVD and CKD events.We included all participants with complete risk factor (covariate data, baseline eGFR > 60 ml/min/1.73 m2, and a confirmed (>3 months apart eGFR 1%-5%, >5% and fitted Poisson models to assess whether CVD and CKD risk group effects were multiplicative. A total of 27,215 participants contributed 202,034 person-years of follow-up: 74% male, median (IQR age 42 (36, 49 years, median (IQR baseline year of follow-up 2005 (2004, 2008. D:A:D risk equations predicted 3,560 (13.1% participants at high CVD risk, 4,996 (18.4% participants at high CKD risk, and 1,585 (5.8% participants at both high CKD and high CVD risk. CVD and CKD event rates by predicted risk group were multiplicative. Participants at high CVD risk had a 5.63-fold (95% CI 4.47, 7.09, p < 0.001 increase in CKD events compared to those at low risk; participants at high CKD risk had a 1.31-fold (95% CI 1.09, 1.56, p = 0.005 increase in CVD events compared to those at low risk. Participants' CVD and CKD risk groups had multiplicative predictive effects, with no evidence of an interaction (p = 0.329 and p = 0.291 for CKD and CVD, respectively. The main study limitation is the difference in the ascertainment of the clinically defined CVD endpoints and the laboratory-defined CKD endpoints.We found that people at high predicted risk for both CVD and CKD have substantially greater risks for both CVD and CKD events compared with those at low predicted risk for both outcomes, and compared to those at high predicted risk for only CVD or CKD events. This suggests that CVD and

  10. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  11. Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations.

    Directory of Open Access Journals (Sweden)

    Zefeng Zhang

    Full Text Available The American College of Cardiology/American Heart Association developed Pooled Cohort equations to estimate atherosclerotic cardiovascular disease (ASCVD risk. It is unclear how well the equations predict ASCVD mortality in a nationally representative cohort. We used the National Health and Nutrition Examination Survey (NHANES 1988-1994 and Linked Mortality through 2006 (n = 6,644. Among participants aged 40-79 years without ASCVD at baseline, we used Cox proportional hazard models to estimate the 10-year probability of ASCVD death by sex and race-ethnicity (non-Hispanic white (NHW, non-Hispanic black (NHB and Mexican American (MA. We estimated the discrimination and calibration for each sex-race-ethnicity model. We documented 288 ASCVD deaths during 62,335 person years. The Pooled Cohort equations demonstrated moderate to good discrimination for ASCVD mortality, with modified C-statistics of 0.716 (95% CI 0.663-0.770, 0.794 (0.734-0.854, and 0.733 (0.654-0.811 for NHW, NHB and MA men, respectively. The corresponding C-statistics for women were 0.781 (0.718-0.844, 0.702 (0.633-0.771, and 0.789 (CI 0.721-0.857. Modified Hosmer-Lemeshow χ2 suggested adequate calibration for NHW, NHB and MA men, and MA women (p-values: 0.128, 0.295, 0.104 and 0.163 respectively. The calibration was inadequate for NHW and NHB women (p<0.05. In this nationally representative cohort, the Pooled Cohort equations performed adequately to predict 10-year ASCVD mortality for NHW and NHB men, and MA population, but not for NHW and NHB women.

  12. Role of γ-glutamyl transferase levels in prediction of high cardiovascular risk among patients with non-alcoholic fatty liver disease

    Directory of Open Access Journals (Sweden)

    Benan Kasapoglu

    2016-01-01

    among patients with fatty liver disease should be regarded as a sign of increased cardiovascular disease risk. Larger studies are warranted to elucidate the role of GGT in prediction of cardiovascular risk.

  13. Risk factors for cardiovascular disease and type 2 diabetes retained from childhood to adulthood predict adult outcomes: the Princeton LRC Follow-up Study

    Directory of Open Access Journals (Sweden)

    Morrison John A

    2012-04-01

    Full Text Available Abstract Background Pediatric risk factors predict adult cardiovascular disease (CVD and type 2 diabetes (T2DM, but whether they predict events independently of adult risk factors is not fully known. Objective Assess whether risk factors for CVD and T2DM retained from childhood to adulthood predict CVD and T2DM in young adulthood. Study design 770 schoolchildren, ages 5–20 (mean age 12, 26-yr prospective follow-up. We categorized childhood and adult risk factors and 26-year changes (triglycerides [TG], LDL cholesterol, BMI, blood pressure [BP] and glucose ≥, and HDL cholesterol Results Children who had high TG and retained high TG as adults had increased CVD events as adults (p = .0005. Children who had normal BMI and retained normal BMI as adults had reduced CVD events as adults (p = .02. Children who had high BP or high TG and retained these as adults had increased T2DM as adults (p = .0006, p = .003. Conclusions Risk factors for CVD and T2DM retained from childhood to adulthood predict CVD and T2DM in young adulthood and support universal childhood screening.

  14. Risks for Heart Disease & Stroke

    Science.gov (United States)

    ... Prevent Risks for Heart Disease & Stroke Risks for Heart Disease & Stroke About 1.5 million heart attacks and ... can’t change some of your risks for heart disease and stroke, but you can manage many of ...

  15. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Waist Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular Disease Risk Factors among Peruvian Adults.

    Science.gov (United States)

    Knowles, K M; Paiva, L L; Sanchez, S E; Revilla, L; Lopez, T; Yasuda, M B; Yanez, N D; Gelaye, B; Williams, M A

    2011-01-24

    Objectives. To examine the extent to which measures of adiposity can be used to predict selected components of metabolic syndrome (MetS) and elevated C-reactive protein (CRP). Methods. A total of 1,518 Peruvian adults were included in this study. Waist circumference (WC), body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHtR), and visceral adiposity index (VAI) were examined. The prevalence of each MetS component was determined according to tertiles of each anthropometric measure. ROC curves were used to evaluate the extent to which measures of adiposity can predict cardiovascular risk. Results. All measures of adiposity had the strongest correlation with triglyceride concentrations (TG). For both genders, as adiposity increased, the prevalence of Mets components increased. Compared to individuals with low-BMI and low-WC, men and women with high-BMI and high- WC had higher odds of elevated fasting glucose, blood pressure, TG, and reduced HDL, while only men in this category had higher odds of elevated CRP. Overall, the ROCs showed VAI, WC, and WHtR to be the best predictors for individual MetS components. Conclusions. The results of our study showed that measures of adiposity are correlated with cardiovascular risk although no single adiposity measure was identified as the best predictor for MetS.

  17. Waist Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular Disease Risk Factors among Peruvian Adults

    Directory of Open Access Journals (Sweden)

    K. M. Knowles

    2011-01-01

    Full Text Available Objectives. To examine the extent to which measures of adiposity can be used to predict selected components of metabolic syndrome (MetS and elevated C-reactive protein (CRP. Methods. A total of 1,518 Peruvian adults were included in this study. Waist circumference (WC, body mass index (BMI, waist-hip ratio (WHR, waist-height ratio (WHtR, and visceral adiposity index (VAI were examined. The prevalence of each MetS component was determined according to tertiles of each anthropometric measure. ROC curves were used to evaluate the extent to which measures of adiposity can predict cardiovascular risk. Results. All measures of adiposity had the strongest correlation with triglyceride concentrations (TG. For both genders, as adiposity increased, the prevalence of Mets components increased. Compared to individuals with low-BMI and low-WC, men and women with high-BMI and high- WC had higher odds of elevated fasting glucose, blood pressure, TG, and reduced HDL, while only men in this category had higher odds of elevated CRP. Overall, the ROCs showed VAI, WC, and WHtR to be the best predictors for individual MetS components. Conclusions. The results of our study showed that measures of adiposity are correlated with cardiovascular risk although no single adiposity measure was identified as the best predictor for MetS.

  18. Incremental value of a genetic risk score for the prediction of new vascular events in patients with clinically manifest vascular disease.

    Science.gov (United States)

    Weijmans, Maaike; de Bakker, Paul I W; van der Graaf, Yolanda; Asselbergs, Folkert W; Algra, Ale; Jan de Borst, Gert; Spiering, Wilko; Visseren, Frank L J

    2015-04-01

    Several genetic markers are related to incidence of cardiovascular events. We evaluated whether a genetic risk score (GRS) based on 30 single-nucleotide-polymorphisms associated with coronary artery disease (CAD) can improve prediction of 10-year risk of new cardiovascular events in patients with clinical manifest vascular disease. In 5742 patients with symptomatic vascular disease enrolled in the SMART study, we developed Cox regression models based on the SMART Risk Score (SRS) and based on the SRS plus the GRS in all patients, in patients with a history of acute arterial thrombotic events and in patients with a history of more stable atherosclerosis and without CAD. The discriminatory ability was expressed by the c-statistic. Model calibration was evaluated by calibration plots. The incremental value of adding the GRS was assessed by net reclassification index (NRI) and decision curve analysis. During a median follow-up of 6.5 years (IQR4.0-9.5), the composite outcome of myocardial infarction, stroke, or vascular death occurred in 933 patients. Hazard ratios of GRS ranging from 0.86 to 1.35 were observed. The discriminatory capacity of the SRS for prediction of 10-year risk of cardiovascular events was fairly good (c-statistic 0.70, 95%CI 0.68-0.72), similar to the model based on the SRS plus the GRS. Calibration of the models based on SRS and SRS plus GRS was adequate. No increase in c-statistics, categorical NRIs and decision curves was observed when adding the GRS. The continuous NRI improved only in patients with stable atherosclerosis (0.14, 95%CI 0.03-0.25), increasing further excluding patients with a history of CAD (0.21, 95%CI 0.06-0.36). In patients with symptomatic vascular disease, a GRS did not improve risk prediction of 10-year risk of cardiovascular events beyond clinical characteristics. The GRS might improve risk prediction of first vascular events in the subgroup of patients with a history of stable atherosclerosis. Copyright © 2015 Elsevier

  19. Risk of cardiovascular disease

    DEFF Research Database (Denmark)

    Gejl, Michael; Starup-Linde, Jakob; Scheel-Thomsen, Jan

    2014-01-01

    AIMS: Type 2 diabetes (DM) increases the risk of cardiovascular disease. We investigated the effects of antidiabetic drugs on the composite endpoint (CE) of ischemic heart disease, heart failure or stroke in DM patients. METHODS: We conducted a nested case-control study. Cases were DM patients who......% CI: 16.88-24.12), neuropathy (OR=1.39, 95% CI: 1.05-1.85) and peripheral artery disease (OR=1.31, 95% CI: 1.02-1.69) increased the risk of CE. Biguanides (OR=0.62 95% CI; 0.54-0.71) and liraglutide (OR=0.48 95% CI; 0.38-0.62) significantly decreased the risk of CE as did statin treatment (OR=0.63, 95...

  20. Common carotid artery intima-media thickness is as good as carotid intima-media thickness of all carotid artery segments in improving prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study.

    Science.gov (United States)

    Nambi, Vijay; Chambless, Lloyd; He, Max; Folsom, Aaron R; Mosley, Tom; Boerwinkle, Eric; Ballantyne, Christie M

    2012-01-01

    Carotid intima-media thickness (CIMT) and plaque information can improve coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). However, obtaining adequate images of all carotid artery segments (A-CIMT) may be difficult. Of A-CIMT, the common carotid artery intima-media thickness (CCA-IMT) is relatively more reliable and easier to measure. We evaluated whether CCA-IMT is comparable to A-CIMT when added to TRF and plaque information in improving CHD risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. Ten-year CHD risk prediction models using TRF alone, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque were developed for the overall cohort, men, and women. The area under the receiver operator characteristic curve (AUC), per cent individuals reclassified, net reclassification index (NRI), and model calibration by the Grønnesby-Borgan test were estimated. There were 1722 incident CHD events in 12 576 individuals over a mean follow-up of 15.2 years. The AUC for TRF only, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque models were 0.741, 0.754, and 0.753, respectively. Although there was some discordance when the CCA-IMT + plaque- and A-CIMT + plaque-based risk estimation was compared, the NRI and clinical NRI (NRI in the intermediate-risk group) when comparing the CIMT models with TRF-only model, per cent reclassified, and test for model calibration were not significantly different. Coronary heart disease risk prediction can be improved by adding A-CIMT + plaque or CCA-IMT + plaque information to TRF. Therefore, evaluating the carotid artery for plaque presence and measuring CCA-IMT, which is easier and more reliable than measuring A-CIMT, provide a good alternative to measuring A-CIMT for CHD risk prediction.

  1. Predicting Long-term Ischemic Events Using Routine Clinical Parameters in Patients with Coronary Artery Disease: The OPT-CAD Risk Score.

    Science.gov (United States)

    Han, Yaling; Chen, Jiyan; Qiu, Miaohan; Li, Yi; Li, Jing; Feng, Yingqing; Qiu, Jian; Meng, Liang; Sun, Yihong; Tao, Guizhou; Wu, Zhaohui; Yang, Chunyu; Guo, Jincheng; Pu, Kui; Chen, Shaoliang; Wang, Xiaozeng

    2018-06-05

    The prognosis of patients with coronary artery disease (CAD) at hospital discharge was constantly varying, and post-discharge risk of ischemic events remain a concern. However, risk prediction tools to identify risk of ischemia for these patients has not yet been reported. We sought to develop a scoring system for predicting long-term ischemic events in CAD patients receiving antiplatelet therapy that would be beneficial in appropriate personalized decision-making for these patients. In this prospective Optimal antiPlatelet Therapy for Chinese patients with Coronary Artery Disease (OPT-CAD, NCT01735305) registry, a total of 14,032 patients with CAD receiving at least one kind of antiplatelet agent were enrolled from 107 centers across China, from January 2012 to March 2014. The risk scoring system was developed in a derivation cohort (enrolled initially 10,000 patients in the database) using a logistic regression model and was subsequently tested in a validation cohort (the last 4,032 patients). Points in risk score was assigned based on the multivariable odds ratio of each factor. Ischemic events were defined as the composite of cardiac death, myocardial infarction or stroke. Ischemic events occurred in 342 (3.4%) patients in the derivation cohort and 160 (4.0%) patients in the validation cohort during 1-year follow-up. The OPT-CAD score, ranging from 0-257 points, consist of 10 independent risk factors, including age (0-71 points), heart rates (0-36 points), hypertension (0-20 points), prior myocardial infarction (16 points), prior stroke (16 points), renal insufficient (21 points), anemia (19 points), low ejection fraction (22 points), positive cardiac troponin (23 points) and ST-segment deviation (13 points). In predicting 1-year ischemic events, the area under receiver operating characteristics curve were 0.73 and 0.72 in derivation and validation cohort, respectively. The incidences of ischemic events in low- (0-90 points), medium- (91-150 points) and

  2. Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

    Science.gov (United States)

    Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

    2016-04-01

    The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories ( 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

  3. Chagas disease risk in Texas.

    Science.gov (United States)

    Sarkar, Sahotra; Strutz, Stavana E; Frank, David M; Rivaldi, Chissa-Louise; Sissel, Blake; Sánchez-Cordero, Victor

    2010-10-05

    Chagas disease, caused by Trypanosoma cruzi, remains a serious public health concern in many areas of Latin America, including México. It is also endemic in Texas with an autochthonous canine cycle, abundant vectors (Triatoma species) in many counties, and established domestic and peridomestic cycles which make competent reservoirs available throughout the state. Yet, Chagas disease is not reportable in Texas, blood donor screening is not mandatory, and the serological profiles of human and canine populations remain unknown. The purpose of this analysis was to provide a formal risk assessment, including risk maps, which recommends the removal of these lacunae. The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five-stage analysis. 1. Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers. The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post-1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis (1 arc-minute). The new data extended the distribution of vector species to 10 new Texas counties. The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México, T. lecticularia has a diffuse suitable habitat distribution along both coasts of the same region, and T. sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States. The ecological risk is highest in south Texas. 2. Incidence-based relative risk was computed at the county level using the Bayesian Besag-York-Mollié model and post-1960 T. cruzi incidence data. This risk is concentrated in south Texas. 3. The

  4. Chagas disease risk in Texas.

    Directory of Open Access Journals (Sweden)

    Sahotra Sarkar

    Full Text Available BACKGROUND: Chagas disease, caused by Trypanosoma cruzi, remains a serious public health concern in many areas of Latin America, including México. It is also endemic in Texas with an autochthonous canine cycle, abundant vectors (Triatoma species in many counties, and established domestic and peridomestic cycles which make competent reservoirs available throughout the state. Yet, Chagas disease is not reportable in Texas, blood donor screening is not mandatory, and the serological profiles of human and canine populations remain unknown. The purpose of this analysis was to provide a formal risk assessment, including risk maps, which recommends the removal of these lacunae. METHODS AND FINDINGS: The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five-stage analysis. 1. Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers. The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post-1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis (1 arc-minute. The new data extended the distribution of vector species to 10 new Texas counties. The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México, T. lecticularia has a diffuse suitable habitat distribution along both coasts of the same region, and T. sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States. The ecological risk is highest in south Texas. 2. Incidence-based relative risk was computed at the county level using the Bayesian Besag-York-Mollié model and post-1960 T. cruzi incidence data. This

  5. NT-proBNP is associated with coronary heart disease risk in healthy older women but fails to enhance prediction beyond established risk factors: results from the British Women's Heart and Health Study.

    Science.gov (United States)

    Sattar, Naveed; Welsh, Paul; Sarwar, Nadeem; Danesh, John; Di Angelantonio, Emanuele; Gudnason, Vilmundur; Davey Smith, George; Ebrahim, Shah; Lawlor, Debbie A

    2010-03-01

    Limited evidence suggests NT-proBNP improves prediction of coronary heart disease (CHD) events but further data are needed, especially in people without pre-existing CHD and in women. We measured NT-proBNP in serum from 162 women with incident CHD events and 1226 controls (60-79 years) in a case-control study nested within the prospective British Women's Heart and Health Study. All cases and controls were free from CHD at baseline. We related NT-proBNP to CHD event risk, and determined to what extent NT-proBNP enhanced CHD risk prediction beyond established risk factors. The odds ratio for CHD per 1 standard deviation increase in log(e)NT-proBNP was 1.37 (95% CI: 1.13-1.68) in analyses adjusted for established CHD risk factors, social class, CRP and insulin. However, addition of log(e)NT-proBNP did not improve the discrimination of a prediction model including age, social class, smoking, physical activity, lipids, fasting glucose, waist:hip ratio, hypertension, statin and aspirin use, nor a standard Framingham risk score model; area under the receiver operator curve for the former model increased from 0.676 to 0.687 on inclusion of NT-proBNP (p=0.3). Furthermore, adding NT-proBNP did not improve calibration of a prediction model containing established risk factors, nor did inclusion more appropriately re-classify participants in relation to their final outcome. Findings were similar (independent associations, but no prediction improvement) for fasting insulin and CRP. These results caution against use of NT-proBNP for CHD risk prediction in healthy women and suggest a need for larger studies in both genders to resolve outstanding uncertainties.

  6. Developmental dyslexia: predicting individual risk.

    Science.gov (United States)

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  7. Increased NT-proANP predicts risk of congestive heart failure in Cavalier King Charles spaniels with mitral regurgitation caused by myxomatous valve disease.

    Science.gov (United States)

    Eriksson, Anders S; Häggström, Jens; Pedersen, Henrik Duelund; Hansson, Kerstin; Järvinen, Anna-Kaisa; Haukka, Jari; Kvart, Clarence

    2014-09-01

    To evaluate the predictive value of plasma N-terminal pro-atrial natriuretic peptide (NT-proANP) and nitric oxide end-products (NOx) as markers for progression of mitral regurgitation caused by myxomatous mitral valve disease. Seventy-eight privately owned Cavalier King Charles spaniels with naturally occurring myxomatous mitral valve disease. Prospective longitudinal study comprising 312 measurements over a 4.5 year period. Clinical values were recorded, NT-proANP concentrations were measured by radioimmunoassay, and NOx were analyzed colorimetrically. To predict congestive heart failure (CHF), Cox proportional hazards models with time-varying covariates were constructed. The hazard ratio for NT-proANP (per 1000 pmol/l increase) to predict future CHF was 6.7 (95% confidence interval, 3.6-12.5; p 1000 pmol/l was 11 months (95% confidence interval, 5.6-12.6 months), compared to 54 months (46 - infinity) for dogs with concentrations ≤ 1000 pmol/l (p 130 beats per minute) and grade of murmur (≥ 3/6). The risk of CHF due to mitral regurgitation is increased in dogs with blood NT-proANP concentrations above 1000 pmol/l. Measurement of NT-proANP can be a valuable tool to identify dogs that may develop CHF within months. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Volume of Lytic Vertebral Body Metastatic Disease Quantified Using Computed Tomography–Based Image Segmentation Predicts Fracture Risk After Spine Stereotactic Body Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Thibault, Isabelle [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Department of Radiation Oncology, Centre Hospitalier de L' Universite de Québec–Université Laval, Quebec, Quebec (Canada); Whyne, Cari M. [Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Department of Surgery, University of Toronto, Toronto, Ontario (Canada); Zhou, Stephanie; Campbell, Mikki [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Atenafu, Eshetu G. [Department of Biostatistics, University Health Network, University of Toronto, Toronto, Ontario (Canada); Myrehaug, Sten; Soliman, Hany; Lee, Young K. [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Ebrahimi, Hamid [Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Department of Surgery, University of Toronto, Toronto, Ontario (Canada); Yee, Albert J.M. [Division of Orthopaedic Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Sahgal, Arjun, E-mail: arjun.sahgal@sunnybrook.ca [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada)

    2017-01-01

    Purpose: To determine a threshold of vertebral body (VB) osteolytic or osteoblastic tumor involvement that would predict vertebral compression fracture (VCF) risk after stereotactic body radiation therapy (SBRT), using volumetric image-segmentation software. Methods and Materials: A computational semiautomated skeletal metastasis segmentation process refined in our laboratory was applied to the pretreatment planning CT scan of 100 vertebral segments in 55 patients treated with spine SBRT. Each VB was segmented and the percentage of lytic and/or blastic disease by volume determined. Results: The cumulative incidence of VCF at 3 and 12 months was 14.1% and 17.3%, respectively. The median follow-up was 7.3 months (range, 0.6-67.6 months). In all, 56% of segments were determined lytic, 23% blastic, and 21% mixed, according to clinical radiologic determination. Within these 3 clinical cohorts, the segmentation-determined mean percentages of lytic and blastic tumor were 8.9% and 6.0%, 0.2% and 26.9%, and 3.4% and 15.8% by volume, respectively. On the basis of the entire cohort (n=100), a significant association was observed for the osteolytic percentage measures and the occurrence of VCF (P<.001) but not for the osteoblastic measures. The most significant lytic disease threshold was observed at ≥11.6% (odds ratio 37.4, 95% confidence interval 9.4-148.9). On multivariable analysis, ≥11.6% lytic disease (P<.001), baseline VCF (P<.001), and SBRT with ≥20 Gy per fraction (P=.014) were predictive. Conclusions: Pretreatment lytic VB disease volumetric measures, independent of the blastic component, predict for SBRT-induced VCF. Larger-scale trials evaluating our software are planned to validate the results.

  9. An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study.

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

    2017-02-01

    An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0

  10. Avian Cholera emergence in Arctic-nesting northern Common Eiders: using community-based, participatory surveillance to delineate disease outbreak patterns and predict transmission risk

    Directory of Open Access Journals (Sweden)

    Samuel A. Iverson

    2016-12-01

    Full Text Available Emerging infectious diseases are a growing concern in wildlife conservation. Documenting outbreak patterns and determining the ecological drivers of transmission risk are fundamental to predicting disease spread and assessing potential impacts on population viability. However, evaluating disease in wildlife populations requires expansive surveillance networks that often do not exist in remote and developing areas. Here, we describe the results of a community-based research initiative conducted in collaboration with indigenous harvesters, the Inuit, in response to a new series of Avian Cholera outbreaks affecting Common Eiders (Somateria mollissima and other comingling species in the Canadian Arctic. Avian Cholera is a virulent disease of birds caused by the bacterium Pasteurella multocida. Common Eiders are a valuable subsistence resource for Inuit, who hunt the birds for meat and visit breeding colonies during the summer to collect eggs and feather down for use in clothing and blankets. We compiled the observations of harvesters about the growing epidemic and with their assistance undertook field investigation of 131 colonies distributed over >1200 km of coastline in the affected region. Thirteen locations were identified where Avian Cholera outbreaks have occurred since 2004. Mortality rates ranged from 1% to 43% of the local breeding population at these locations. Using a species-habitat model (Maxent, we determined that the distribution of outbreak events has not been random within the study area and that colony size, vegetation cover, and a measure of host crowding in shared wetlands were significantly correlated to outbreak risk. In addition, outbreak locations have been spatially structured with respect to hypothesized introduction foci and clustered along the migration corridor linking Arctic breeding areas with wintering areas in Atlantic Canada. At present, Avian Cholera remains a localized threat to Common Eider populations in the

  11. Donor genotype in the Interleukin-7 receptor α-chain predicts risk of graft-versus-host disease and cytomegalovirus infection after allogeneic hematopoietic stem cell transplantation

    DEFF Research Database (Denmark)

    Kielsen, Katrine; Enevold, Christian; Heilmann, Carsten

    2018-01-01

    The efficacy of allogeneic hematopoietic stem cell transplantation (HSCT) is challenged by acute and chronic graft-versus-host disease (aGVHD and cGVHD) and viral infections due to long-lasting immunodeficiency. Interleukin-7 (IL-7) is a cytokine essential for de novo T cell generation in thymus.......1-3.8, P = 0.034) and with significantly increased risk of extensive cGVHD (HR = 2.0, 95% CI = 1.1-3.6, P = 0.025) after adjustment for potential risk factors. In addition, the TT genotype was associated with a higher risk of cytomegalovirus (CMV) infection post-transplant (HR = 2.4, 95% CI = 1.2-4.3, P.......7, 95% CI = 1.2-2.3, P = 0.0027) and increased treatment-related mortality (HR = 2.3, 95% CI = 1.3-4.0, P = 0.0047), but was not associated with the risk of relapse (P = 0.35). In conclusion, the IL-7Rα rs6897932 genotype of the donor is predictive of aGVHD and cGVHD, CMV infection, and mortality...

  12. Clinical Utility of a Coronary Heart Disease Risk Prediction Gene Score in UK Healthy Middle Aged Men and in the Pakistani Population.

    Directory of Open Access Journals (Sweden)

    Katherine E Beaney

    Full Text Available Numerous risk prediction algorithms based on conventional risk factors for Coronary Heart Disease (CHD are available but provide only modest discrimination. The inclusion of genetic information may improve clinical utility.We tested the use of two gene scores (GS in the prospective second Northwick Park Heart Study (NPHSII of 2775 healthy UK men (284 cases, and Pakistani case-control studies from Islamabad/Rawalpindi (321 cases/228 controls and Lahore (414 cases/219 controls. The 19-SNP GS included SNPs in loci identified by GWAS and candidate gene studies, while the 13-SNP GS only included SNPs in loci identified by the CARDIoGRAMplusC4D consortium.In NPHSII, the mean of both gene scores was higher in those who went on to develop CHD over 13.5 years of follow-up (19-SNP p=0.01, 13-SNP p=7x10-3. In combination with the Framingham algorithm the GSs appeared to show improvement in discrimination (increase in area under the ROC curve, 19-SNP p=0.48, 13-SNP p=0.82 and risk classification (net reclassification improvement (NRI, 19-SNP p=0.28, 13-SNP p=0.42 compared to the Framingham algorithm alone, but these were not statistically significant. When considering only individuals who moved up a risk category with inclusion of the GS, the improvement in risk classification was statistically significant (19-SNP p=0.01, 13-SNP p=0.04. In the Pakistani samples, risk allele frequencies were significantly lower compared to NPHSII for 13/19 SNPs. In the Islamabad study, the mean gene score was higher in cases than controls only for the 13-SNP GS (2.24 v 2.34, p=0.04. There was no association with CHD and either score in the Lahore study.The performance of both GSs showed potential clinical utility in European men but much less utility in subjects from Pakistan, suggesting that a different set of risk loci or SNPs may be required for risk prediction in the South Asian population.

  13. Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients

    Science.gov (United States)

    Hayano, Junichiro; Yasuma, Fumihiko; Watanabe, Eiichi; Carney, Robert M.; Stein, Phyllis K.; Blumenthal, James A.; Arsenos, Petros; Gatzoulis, Konstantinos A.; Takahashi, Hiroshi; Ishii, Hideki; Kiyono, Ken; Yamamoto, Yoshiharu; Yoshida, Yutaka; Yuda, Emi; Kodama, Itsuo

    2017-01-01

    Abstract Aims Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing is thought to reflect cardiac autonomic responses to apnoeic/hypoxic stress. We examined whether blunted CVHR observed in ambulatory ECG could predict the mortality risk. Methods and results CVHR in night-time Holter ECG was detected by an automated algorithm, and the prognostic relationships of the frequency (FCV) and amplitude (ACV) of CVHR were examined in 717 patients after myocardial infarction (post-MI 1, 6% mortality, median follow-up 25 months). The predictive power was prospectively validated in three independent cohorts: a second group of 220 post-MI patients (post-MI 2, 25.5% mortality, follow-up 45 months); 299 patients with end-stage renal disease on chronic haemodialysis (ESRD, 28.1% mortality, follow-up 85 months); and 100 patients with chronic heart failure (CHF, 35% mortality, follow-up 38 months). Although CVHR was observed in ≥96% of the patients in all cohorts, FCV did not predict mortality in any cohort. In contrast, decreased ACV was a powerful predictor of mortality in the post-MI 1 cohort (hazard ratio [95% CI] per 1 ln [ms] decrement, 2.9 [2.2–3.7], P < 0.001). This prognostic relationship was validated in the post-MI 2 (1.8 [1.4–2.2], P < 0.001), ESRD (1.5 [1.3–1.8], P < 0.001), and CHF (1.4 [1.1–1.8], P = 0.02) cohorts. The prognostic value of ACV was independent of age, gender, diabetes, β-blocker therapy, left ventricular ejection fraction, sleep-time mean R-R interval, and FCV. Conclusion Blunted CVHR detected by decreased ACV in a night-time Holter ECG predicts increased mortality risk in post-MI, ESRD, and CHF patients. PMID:27789562

  14. Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients.

    Science.gov (United States)

    Hayano, Junichiro; Yasuma, Fumihiko; Watanabe, Eiichi; Carney, Robert M; Stein, Phyllis K; Blumenthal, James A; Arsenos, Petros; Gatzoulis, Konstantinos A; Takahashi, Hiroshi; Ishii, Hideki; Kiyono, Ken; Yamamoto, Yoshiharu; Yoshida, Yutaka; Yuda, Emi; Kodama, Itsuo

    2017-08-01

    Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing is thought to reflect cardiac autonomic responses to apnoeic/hypoxic stress. We examined whether blunted CVHR observed in ambulatory ECG could predict the mortality risk. CVHR in night-time Holter ECG was detected by an automated algorithm, and the prognostic relationships of the frequency (FCV) and amplitude (ACV) of CVHR were examined in 717 patients after myocardial infarction (post-MI 1, 6% mortality, median follow-up 25 months). The predictive power was prospectively validated in three independent cohorts: a second group of 220 post-MI patients (post-MI 2, 25.5% mortality, follow-up 45 months); 299 patients with end-stage renal disease on chronic haemodialysis (ESRD, 28.1% mortality, follow-up 85 months); and 100 patients with chronic heart failure (CHF, 35% mortality, follow-up 38 months). Although CVHR was observed in ≥96% of the patients in all cohorts, FCV did not predict mortality in any cohort. In contrast, decreased ACV was a powerful predictor of mortality in the post-MI 1 cohort (hazard ratio [95% CI] per 1 ln [ms] decrement, 2.9 [2.2-3.7], P < 0.001). This prognostic relationship was validated in the post-MI 2 (1.8 [1.4-2.2], P < 0.001), ESRD (1.5 [1.3-1.8], P < 0.001), and CHF (1.4 [1.1-1.8], P = 0.02) cohorts. The prognostic value of ACV was independent of age, gender, diabetes, β-blocker therapy, left ventricular ejection fraction, sleep-time mean R-R interval, and FCV. Blunted CVHR detected by decreased ACV in a night-time Holter ECG predicts increased mortality risk in post-MI, ESRD, and CHF patients. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

  15. Prevalence of cardiovascular risks factors and 10 year predictions of coronary heart disease in seafarers of Pertamina shipping (Indonesia)

    DEFF Research Database (Denmark)

    Purnawarma, Irwin GNI; Jensen, Olaf Chresten; Canals, ML

    2011-01-01

    independent CHD risk factors were Obesity (BMI >25%, 56%), dislipidemia (TC >200mg/dL and/or TG >150mg/dL, 72.1%), Smoking (43.7%) and Lack of Exercise (43.7%). After adjusting age and comparison to BMI and Cholesterol levels, in older age group seafarers (50-55 yrs old), the risk of a cardiovascular event...

  16. A comparative analysis of cardiovascular disease risk profiles of five Pacific ethnic groups assessed in New Zealand primary care practice: PREDICT CVD-13.

    Science.gov (United States)

    Grey, Corina; Wells, Sue; Riddell, Tania; Pylypchuk, Romana; Marshall, Roger; Drury, Paul; Elley, Raina; Ameratunga, Shanthi; Gentles, Dudley; Erick-Peletiy, Stephanie; Bell, Fionna; Kerr, Andrew; Jackson, Rod

    2010-11-05

    Data on the cardiovascular disease risk profiles of Pacific peoples in New Zealand is usually aggregated and treated as a single entity. Little is known about the comparability or otherwise of cardiovascular disease (CVD) risk between different Pacific groups. To compare CVD risk profiles for the main Pacific ethnic groups assessed in New Zealand primary care practice to determine if it is reasonable to aggregate these data, or if significant differences exist. A web-based clinical decision support system for CVD risk assessment and management (PREDICT) has been implemented in primary care practices in nine PHOs throughout Auckland and Northland since 2002, covering approximately 65% of the population of these regions. Between 2002 and January 2009, baseline CVD risk assessments were carried out on 11,642 patients aged 35-74 years identifying with one or more Pacific ethnic groups (4933 Samoans, 1724 Tongans, 1366 Cook Island Maori, 880 Niueans, 1341 Fijians and 1398 people identified as Other Pacific or Pacific Not Further Defined). Fijians were subsequently excluded from the analyses because of a probable misclassification error that appears to combine Fijian Indians with ethnic Fijians. Prevalences of smoking, diabetes and prior history of CVD, as well as mean total cholesterol/HDL ratio, systolic and diastolic blood pressures, and Framingham 5-year CVD risk were calculated for each Pacific group. Age-adjusted risk ratios and mean differences stratified by gender were calculated using Samoans as the reference group. Cook Island women were almost 60% more likely to smoke than Samoan women. While Tongan men had the highest proportion of smoking (29%) among Pacific men, Tongan women had the lowest smoking proportion (10%) among Pacific women. Tongan women and Niuean men and women had a higher burden of diabetes than other Pacific ethnic groups, which were 20-30% higher than their Samoan counterparts. Niuean men and women had lower blood pressure levels than all

  17. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review

    NARCIS (Netherlands)

    van Dieren, S.; Beulens, J. W. J.; Kengne, A. P.; Peelen, L. M.; Rutten, G. E. H. M.; Woodward, M.; van der Schouw, Y. T.; Moons, K. G. M.

    2012-01-01

    A recent overview of all CVD models applicable to diabetes patients is not available. To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes.

  18. Mutation in APOA1 predicts increased risk of ischaemic heart disease and total mortality without low HDL cholesterol levels

    DEFF Research Database (Denmark)

    Haase, C L; Frikke-Schmidt, R; Nordestgaard, B G

    2011-01-01

    levels. Mutations in apolipoprotein (apo) A-I, the major protein constituent of HDL, might be associated with low HDL cholesterol and predispose to IHD and early death. DESIGN: We resequenced APOA1 in 190 individuals and examined the effect of mutations on HDL cholesterol, risk of IHD, myocardial...

  19. Risk prediction in stable cardiovascular disease using a high-sensitivity cardiac troponin T single biomarker strategy compared to the ESC-SCORE.

    Science.gov (United States)

    Biener, Moritz; Giannitsis, Evangelos; Kuhner, Manuel; Zelniker, Thomas; Mueller-Hennessen, Matthias; Vafaie, Mehrshad; Stoyanov, Kiril M; Neumann, Franz-Josef; Katus, Hugo A; Hochholzer, Willibald; Valina, Christian Marc

    2018-01-01

    To evaluate the prognostic performance of high-sensitivity cardiac troponin T (hs-cTnT) compared with the ESC-SCORE. We included low-risk outpatients with stable cardiovascular (CV) disease categorised into need for non-secondary and secondary prevention. The prognostication of hs-cTnT at index visit was compared with the European Society of Cardiology-Systematic COronary Risk Evaluation (ESC-SCORE) with respect to all-cause mortality (ACM) and two composite endpoints (ACM, acute myocardial infarction (AMI) and stroke and ACM, AMI, stroke and rehospitalisation for acute coronary syndrome (ACS) and decompensated heart failure (DHF)). Within a median follow-up of 796 days, a total of 16 deaths, 32 composite endpoints of ACM, AMI and stroke and 83 composite endpoints of ACM, AMI, stroke, rehospitalisation for ACS and DHF were observed among 693 stable low-risk outpatients. Using C-statistics, measurement of hs-cTnT alone outperformed the ESC-SCORE for the prediction of ACM in the entire study population (Δarea under the curve (AUC) 0.221, p=0.0039) and both prevention groups (non-secondary: ΔAUC 0.164, p=0.0208; secondary: ΔAUC 0.264, p=0.0134). For the prediction of all other secondary endpoints, hs-cTnT was at least as effective as the ESC-SCORE, both in secondary and non-secondary prevention. Using continuous and categorical net reclassification improvement and integrated discrimination improvement, hs-cTnT significantly improved reclassification regarding all endpoints in the entire population and in the secondary prevention cohort. In non-secondary prevention, hs-cTnT improved reclassification only for ACM. The results were confirmed in an independent external cohort on 2046 patients. Hs-cTnT is superior to the multivariable ESC-SCORE for the prediction of ACM and a composite endpoint in stable outpatients with and without relevant CV disease. NCT01954303; Pre-results.

  20. Does IQ predict total and cardiovascular disease mortality as strongly as other risk factors? Comparison of effect estimates using the Vietnam Experience Study

    DEFF Research Database (Denmark)

    Batty, G D; Shipley, M J; Gale, C R

    2008-01-01

    To compare the strength of the relation of two measurements of IQ and 11 established risk factors with total and cardiovascular disease (CVD) mortality.......To compare the strength of the relation of two measurements of IQ and 11 established risk factors with total and cardiovascular disease (CVD) mortality....

  1. Waist Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular Disease Risk Factors among Peruvian Adults

    OpenAIRE

    Knowles, K. M.; Paiva, L. L.; Sanchez, S. E.; Revilla, L.; Lopez, T.; Yasuda, M. B.; Yanez, N. D.; Gelaye, B.; Williams, M. A.

    2011-01-01

    Objectives. To examine the extent to which measures of adiposity can be used to predict selected components of metabolic syndrome (MetS) and elevated C-reactive protein (CRP). Methods. A total of 1,518 Peruvian adults were included in this study. Waist circumference (WC), body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHtR), and visceral adiposity index (VAI) were examined. The prevalence of each MetS component was determined according to tertiles of each anthropometric mea...

  2. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  3. 20. Prediction of 10-year risk of hard coronary events among Saudi adults based on prevalence of heart disease risk factors

    Directory of Open Access Journals (Sweden)

    Muhammad Adil Soofi

    2015-10-01

    Conclusions: Our study is the first to estimate the 10-year risk of HCE among adults in an emerging country and discovered a significant proportion of younger aged population are at risk for development of hard coronary events. Public awareness programs to control risk factors are warranted.

  4. High RBM3 expression in prostate cancer independently predicts a reduced risk of biochemical recurrence and disease progression

    Directory of Open Access Journals (Sweden)

    Bjartell Anders

    2011-09-01

    Full Text Available Abstract Background High expression of the RNA-binding protein RBM3 has previously been found to be associated with good prognosis in breast cancer, ovarian cancer, malignant melanoma and colorectal cancer. The aim of this study was to examine the prognostic impact of immunohistochemical RBM3 expression in prostate cancer. Findings Immunohistochemical RBM3 expression was examined in a tissue microarray with malignant and benign prostatic specimens from 88 patients treated with radical prostatectomy for localized disease. While rarely expressed in benign prostate gland epithelium, RBM3 was found to be up-regulated in prostate intraepithelial neoplasia and present in various fractions and intensities in invasive prostate cancer. High nuclear RBM3 expression was significantly associated with a prolonged time to biochemical recurrence (BCR (HR 0.56, 95% CI: 0.34-0.93, p = 0.024 and clinical progression (HR 0.09, 95% CI: 0.01-0.71, p = 0.021. These associations remained significant in multivariate analysis, adjusted for preoperative PSA level in blood, pathological Gleason score and presence or absence of extracapsular extension, seminal vesicle invasion and positive surgical margin (HR 0.41, 95% CI: 0.19-0.89, p = 0.024 for BCR and HR 0.06, 95% CI: 0.01-0.50, p = 0.009 for clinical progression. Conclusion Our results demonstrate that high nuclear expression of RBM3 in prostate cancer is associated with a prolonged time to disease progression and, thus, a potential biomarker of favourable prognosis. The value of RBM3 for prognostication, treatment stratification and follow-up of prostate cancer patients should be further validated in larger studies.

  5. Wildlife disease and risk perception.

    Science.gov (United States)

    Hanisch-Kirkbride, Shauna L; Riley, Shawn J; Gore, Meredith L

    2013-10-01

    Risk perception has an important influence on wildlife management and is particularly relevant to issues that present health risks, such as those associated with wildlife disease management. Knowledge of risk perceptions is useful to wildlife health professionals in developing communication messages that enhance public understanding of wildlife disease risks and that aim to increase public support for disease management. To promote knowledge of public understanding of disease risks in the context of wildlife disease management, we used a self-administered questionnaire mailed to a stratified random sample (n = 901) across the continental United States to accomplish three objectives: 1) assess zoonotic disease risk perceptions; 2) identify sociodemographic and social psychologic factors underlying these risk perceptions; and 3) examine the relationship between risk perception and agreement with wildlife disease management practices. Diseases we assessed in the surveys were rabies, plague, and West Nile virus. Risk perception, as measured by an index consisting of severity, susceptibility, and dread, was greatest for rabies and West Nile virus disease (x = 2.62 and 2.59, respectively, on a scale of 1 to 4 and least for plague (x = 2.39). The four most important variables associated with disease risk perception were gender, education, prior exposure to the disease, and concern for health effects. We found that stronger risk perception was associated with greater agreement with wildlife disease management. We found particular concern for the vulnerability of wildlife to zoonotic disease and for protection of wildlife health, indicating that stakeholders may be receptive to messages emphasizing the potential harm to wildlife from disease and to messages promoting One Health (i.e., those that emphasize the interdependence of human, domestic animal, wildlife, and ecosystem health).

  6. Predictive value of cord blood bilirubins for hyperbilirubinemia in neonates at risk for maternal-fetal blood group incompatibility and hemolytic disease of the newborn

    Science.gov (United States)

    Calkins, Kara L.; Roy, Devika; Molchan, Lauren; Bradley, Lyndsey; Grogan, Tristan; Elashoff, David; Walker, Valencia P.

    2015-01-01

    Objective To determine the predictive ability of cord blood bilirubin (CBB) for hyperbilirubinemia in a population at risk for maternal-fetal blood group incompatibility and hemolytic disease of the newborn. Study Design This is a single center retrospective case-control study. Cases received phototherapy; controls did not. Cases were matched 1:3 to controls by gender and treating physician. Inclusion criteria included: ≥ 35 weeks gestation, CBB, and one or more total serum bilirubin (TSB) concentrations. The primary outcome was CBB. Secondary outcomes were a TSB > 75th percentile, length of stay, and neonatal intensive care unit admission. The prognostic ability of CBB for phototherapy and TSB > 75th percentile was assessed using area under the receiver operating characteristic (ROC) curve. Logistic regression analyses were performed to determine predictors for phototherapy and TSB > 75th percentile. Result When compared to controls (n=142), cases (n=54) were more likely to have a positive Coombs’ test (82% vs. 41%, p 75th percentile (85% vs. 21%, p75th percentile was 0.87±0.03 (phemolytic disease of the newborn. PMID:26518407

  7. Predictive value of cord blood bilirubin for hyperbilirubinemia in neonates at risk for maternal-fetal blood group incompatibility and hemolytic disease of the newborn.

    Science.gov (United States)

    Calkins, K; Roy, D; Molchan, L; Bradley, L; Grogan, T; Elashoff, D; Walker, V

    2015-01-01

    To determine the predictive ability of cord blood bilirubin (CBB) for hyperbilirubinemia in a population at risk for maternal-fetal blood group incompatibility and hemolytic disease of the newborn. This is a single center retrospective case-control study. Cases received phototherapy; controls did not. Cases were matched 1:3 to controls by gender and treating physician. Inclusion criteria included: ≥35 weeks gestation, CBB, and one or more total serum bilirubin (TSB) concentrations. The primary outcome was CBB. Secondary outcomes were a TSB >75th percentile, length of stay, and neonatal intensive care unit admission. The prognostic ability of CBB for phototherapy and TSB >75th percentile was assessed using area under the receiver operating characteristic (ROC) curve. Logistic regression analyses were performed to determine predictors for phototherapy and TSB >75th percentile. When compared to controls (n = 142), cases (n = 54) were more likely to have a positive Coombs' test (82% vs. 41% , p 75th percentile (85% vs. 21% , p 75th percentile was 0.87 ± 0.03 (p hemolytic disease of the newborn.

  8. Genetic risks for cardiovascular diseases

    NARCIS (Netherlands)

    Zafarmand, M.H.

    2008-01-01

    Atherosclerotic cardiovascular disease (CVD), which involves the heart, brain, and peripheral circulation, is a major health problem world-wide. The development of atherosclerosis is a complex process, and several established risk factors are involved. Nevertheless, these established risk factors

  9. Heart Disease Risk Factors

    Science.gov (United States)

    ... About CDC.gov . Home About Heart Disease Coronary Artery Disease Heart Attack Heart Attack Signs and Symptoms ... Privacy FOIA No Fear Act OIG 1600 Clifton Road Atlanta , GA 30329-4027 USA 800-CDC-INFO ( ...

  10. Immune Biomarkers Predictive for Disease-Free Survival with Adjuvant Sunitinib in High-Risk Locoregional Renal Cell Carcinoma: From Randomized Phase III S-TRAC Study.

    Science.gov (United States)

    George, Daniel J; Martini, Jean-François; Staehler, Michael; Motzer, Robert J; Magheli, Ahmed; Escudier, Bernard; Gerletti, Paola; Li, Sherry; Casey, Michelle; Laguerre, Brigitte; Pandha, Hardev S; Pantuck, Allan J; Patel, Anup; Lechuga, Maria J; Ravaud, Alain

    2018-04-01

    Purpose: Adjuvant sunitinib therapy compared with placebo prolonged disease-free survival (DFS) in patients with locoregional high-risk renal cell carcinoma (RCC) in the S-TRAC trial (ClinicalTrials.gov number NCT00375674). A prospectively designed exploratory analysis of tissue biomarkers was conducted to identify predictors of treatment benefit. Experimental Design: Tissue blocks were used for immunohistochemistry (IHC) staining of programmed cell death ligand 1 (PD-L1), CD4, CD8, and CD68. DFS was compared between < versus ≥ median IHC parameter using the Kaplan-Meier method. For biomarkers with predictive potential, receiver operating characteristics curves were generated. Results: Baseline characteristics were similar in patients with ( n = 191) and without ( n = 419) IHC analysis. Among patients with IHC, longer DFS was observed in patients with tumor CD8 + T-cell density ≥ versus < median [median (95% CI), not reached (6.83-not reached) versus 3.47 years (1.73-not reached); hazard ratio (HR) 0.40 (95% CI, 0.20-0.81); P = 0.009] treated with sunitinib ( n = 101), but not with placebo ( n = 90). The sensitivity and specificity for CD8 + T-cell density in predicting DFS were 0.604 and 0.658, respectively. Shorter DFS was observed in placebo-treated patients with PD-L1 + versus PD-L1 - tumors (HR 1.75; P = 0.103). Among all patients with PD-L1 + tumors, DFS was numerically longer with sunitinib versus placebo (HR 0.58; P = 0.175). Conclusions: Greater CD8 + T-cell density in tumor tissue was associated with longer DFS with sunitinib but not placebo, suggesting predictive treatment effect utility. Further independent cohort validation studies are warranted. The prognostic value of PD-L1 expression in primary tumors from patients with high-risk nonmetastatic RCC should also be further explored. Clin Cancer Res; 24(7); 1554-61. ©2018 AACR . ©2018 American Association for Cancer Research.

  11. Pathway-Specific Aggregate Biomarker Risk Score Is Associated With Burden of Coronary Artery Disease and Predicts Near-Term Risk of Myocardial Infarction and Death

    DEFF Research Database (Denmark)

    Ghasemzedah, Nima; Hayek, Salim; Ko, Yi-An

    2017-01-01

    BACKGROUND: Inflammation, coagulation, and cell stress contribute to atherosclerosis and its adverse events. A biomarker risk score (BRS) based on the circulating levels of biomarkers C-reactive protein, fibrin degradation products, and heat shock protein-70 representing these 3 pathways was a st...

  12. Tail Risk Premia and Return Predictability

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Todorov, Viktor; Xu, Lai

    The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

  13. HeartCare+: A Smart Heart Care Mobile Application for Framingham-Based Early Risk Prediction of Hard Coronary Heart Diseases in Middle East

    Directory of Open Access Journals (Sweden)

    Hoda Ahmed Galal Elsayed

    2017-01-01

    Full Text Available Background. Healthcare is a challenging, yet so demanding sector that developing countries are paying more attention to recently. Statistics show that rural areas are expected to develop a high rate of heart diseases, which is a leading cause of sudden mortality, in the future. Thus, providing solutions that can assist rural people in detecting the cardiac risks early will be vital for uncovering and even preventing the long-term complications of cardiac diseases. Methodology. Mobile technology can be effectively utilized to limit the cardiac diseases’ prevalence in rural Middle East. This paper proposes a smart mobile solution for early risk detection of hard coronary heart diseases that uses the Framingham scoring model. Results. Smart HeartCare+ mobile app estimates accurately coronary heart diseases’ risk over 10 years based on clinical and nonclinical data and classifies the patient risk to low, moderate, or high. HeartCare+ also directs the patients to further treatment recommendations. Conclusion. This work attempts to investigate the effectiveness of the mobile technology in the early risk detection of coronary heart diseases. HeartCare+ app intensifies the communication channel between the lab workers and patients residing in rural areas and cardiologists and specialist residing in urban places.

  14. Huntington's disease : Psychological aspects of predictive testing

    NARCIS (Netherlands)

    Timman, Reinier

    2005-01-01

    Predictive testing for Huntington's disease appears to have long lasting psychological effects. The predictive test for Huntington's disease (HD), a hereditary disease of the nervous system, was introduced in the Netherlands in the late eighties. As adverse consequences of the test were

  15. Performance of Hippocampus Volumetry with FSL-FIRST for Prediction of Alzheimer's Disease Dementia in at Risk Subjects with Amnestic Mild Cognitive Impairment.

    Science.gov (United States)

    Suppa, Per; Hampel, Harald; Kepp, Timo; Lange, Catharina; Spies, Lothar; Fiebach, Jochen B; Dubois, Bruno; Buchert, Ralph

    2016-01-01

    MRI-based hippocampus volume, a core feasible biomarker of Alzheimer's disease (AD), is not yet widely used in clinical patient care, partly due to lack of validation of software tools for hippocampal volumetry that are compatible with routine workflow. Here, we evaluate fully-automated and computationally efficient hippocampal volumetry with FSL-FIRST for prediction of AD dementia (ADD) in subjects with amnestic mild cognitive impairment (aMCI) from phase 1 of the Alzheimer's Disease Neuroimaging Initiative. Receiver operating characteristic analysis of FSL-FIRST hippocampal volume (corrected for head size and age) revealed an area under the curve of 0.79, 0.70, and 0.70 for prediction of aMCI-to-ADD conversion within 12, 24, or 36 months, respectively. Thus, FSL-FIRST provides about the same power for prediction of progression to ADD in aMCI as other volumetry methods.

  16. Cheese and cardiovascular disease risk

    DEFF Research Database (Denmark)

    Hjerpsted, Julie Bousgaard; Tholstrup, Tine

    2016-01-01

    Abstract Currently, the effect of dairy products on cardiovascular risk is a topic with much debate and conflicting results. The purpose of this review is to give an overview of the existing literature regarding the effect of cheese intake and risk of cardiovascular disease (CVD). Studies included...

  17. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

  18. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  19. Lipid measures and cardiovascular disease prediction

    NARCIS (Netherlands)

    van Wijk, D.F.; Stroes, E.S.G.; Kastelein, J.J.P.

    2009-01-01

    Traditional lipid measures are the cornerstone of risk assessment and treatment goals in cardiovascular prevention. Whereas the association between total, LDL-, HDL-cholesterol and cardiovascular disease risk has been generally acknowledged, the rather poor capacity to distinguish between patients

  20. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Developmental Dyslexia: Predicting Individual Risk

    Science.gov (United States)

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  9. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. The Role of Risk Aversion in Predicting Individual Behaviours

    OpenAIRE

    Guiso, Luigi; Paiella, Monica

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers’ decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  13. The Role of Risk Aversion in Predicting Individual Behaviour

    OpenAIRE

    Monica Paiella; Luigi Guiso

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers' decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  14. Entomologic index for human risk of Lyme disease.

    Science.gov (United States)

    Mather, T N; Nicholson, M C; Donnelly, E F; Matyas, B T

    1996-12-01

    An entomologic index based on density estimates of Lyme disease spirochete-infected nymphal deer ticks (lxodes scapularis) was developed to assess human risk of Lyme disease. The authors used a standardized protocol to determine tick density and infection in numerous forested sites in six Rhode Island towns. An entomologic risk index calculated for each town was compared with the number of human Lyme disease cases reported to the Rhode Island State Health Department for the same year. A strong positive relation between entomologic risk index and the Lyme disease case rate for each town suggested that the entomologic index was predictive of Lyme disease risk.

  15. Incremental Value of Repeated Risk Factor Measurements for Cardiovascular Disease Prediction in Middle-Aged Korean Adults: Results From the NHIS-HEALS (National Health Insurance System-National Health Screening Cohort).

    Science.gov (United States)

    Cho, In-Jeong; Sung, Ji Min; Chang, Hyuk-Jae; Chung, Namsik; Kim, Hyeon Chang

    2017-11-01

    Increasing evidence suggests that repeatedly measured cardiovascular disease (CVD) risk factors may have an additive predictive value compared with single measured levels. Thus, we evaluated the incremental predictive value of incorporating periodic health screening data for CVD prediction in a large nationwide cohort with periodic health screening tests. A total of 467 708 persons aged 40 to 79 years and free from CVD were randomly divided into development (70%) and validation subcohorts (30%). We developed 3 different CVD prediction models: a single measure model using single time point screening data; a longitudinal average model using average risk factor values from periodic screening data; and a longitudinal summary model using average values and the variability of risk factors. The development subcohort included 327 396 persons who had 3.2 health screenings on average and 25 765 cases of CVD over 12 years. The C statistics (95% confidence interval [CI]) for the single measure, longitudinal average, and longitudinal summary models were 0.690 (95% CI, 0.682-0.698), 0.695 (95% CI, 0.687-0.703), and 0.752 (95% CI, 0.744-0.760) in men and 0.732 (95% CI, 0.722-0.742), 0.735 (95% CI, 0.725-0.745), and 0.790 (95% CI, 0.780-0.800) in women, respectively. The net reclassification index from the single measure model to the longitudinal average model was 1.78% in men and 1.33% in women, and the index from the longitudinal average model to the longitudinal summary model was 32.71% in men and 34.98% in women. Using averages of repeatedly measured risk factor values modestly improves CVD predictability compared with single measurement values. Incorporating the average and variability information of repeated measurements can lead to great improvements in disease prediction. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02931500. © 2017 American Heart Association, Inc.

  16. Risk profiles of Alzheimer disease.

    Science.gov (United States)

    Bilbul, Melanie; Schipper, Hyman M

    2011-07-01

    Alzheimer disease (AD) is a dementing, neurodegenerative disorder that affects approximately 500,000 Canadians and its prevalence is expected to double over the next 30 years. Although several medications may temporarily augment cognitive abilities in AD, there presently exists no proven method to avoid the inevitable clinical deterioration in this devastating condition. The delineation of risk factors for the development of AD offers hope for the advent of effective prevention or interventions that might retard the onset of symptoms. In this article, we provide a comprehensive review of midlife risk factors implicated in the etiopathogenesis of sporadic AD. Although some risk factors are heritable and largely beyond our control, others are determined by lifestyle or environment and are potentially modifiable. In a companion paper, we introduce the concept of an Alzheimer Risk Assessment Clinic for ascertainment and mitigation of these and other putative dementia risk factors in middle-aged adults.

  17. Prediction of eyespot infection risks

    Directory of Open Access Journals (Sweden)

    M. Váòová

    2012-12-01

    Full Text Available The objective of the study was to design a prediction model for eyespot (Tapesia yallundae infection based on climatic factors (temperature, precipitation, air humidity. Data from experiment years 1994-2002 were used to study correlations between the eyespot infection index and individual weather characteristics. The model of prediction was constructed using multiple regression when a separate parameter is assigned to each factor, i.e. the frequency of days with optimum temperatures, humidity, and precipitation. The correlation between relative air humidity and precipitation and the infection index is significant.

  18. Comparison of cystatin C- and creatinine-based estimated glomerular filtration rate to predict coronary heart disease risk in Japanese patients with obesity and diabetes.

    Science.gov (United States)

    Ito, Ryo; Yamakage, Hajime; Kotani, Kazuhiko; Wada, Hiromichi; Otani, Sumire; Yonezawa, Kazuya; Ogo, Atsushi; Okajima, Taiichiro; Adachi, Masahiro; Araki, Rika; Yoshida, Kazuro; Saito, Miho; Nagaoka, Tadasu; Toyonaga, Tetsushi; Tanaka, Tsuyoshi; Yamada, Tsutomu; Ota, Itsuro; Oishi, Mariko; Miyanaga, Fumiko; Shimatsu, Akira; Satoh-Asahara, Noriko

    2015-01-01

    The aim of this study is to determine which indicator of chronic kidney disease most closely correlates with 10-year Framingham coronary heart disease (CHD) risk among serum creatinine, serum cystatin C (S-CysC), urine albumin-creatinine ratio (UACR), estimated creatinine-based GFRs (eGFRcre), and estimated CysC-based GFRs (eGFRcys) in patients with obesity and diabetes. Serum creatinine, S-CysC, UACR, and cardio-ankle vascular index (CAVI) were examined in 468 outpatients with obesity and type 2 diabetes, free of severe renal dysfunction or previous history of cardiovascular disease, as a cross-sectional survey using baseline data from the multi-centered Japan Diabetes and Obesity Study. S-CysC and eGFRcys had significantly stronger correlations with the 10-year Framingham CHD risk than serum creatinine, eGFRcre, and UACR (creatinine, ρ = 0.318; S-CysC, ρ = 0.497; UACR, ρ = 0.174; eGFRcre, ρ = -0.291; eGFRcys, ρ = -0.521; P obesity and diabetes.

  19. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  20. Low-density lipoprotein cholesterol and risk of gallstone disease

    DEFF Research Database (Denmark)

    Stender, Stefan; Frikke-Schmidt, Ruth; Benn, Marianne

    2013-01-01

    Drugs which reduce plasma low-density lipoprotein cholesterol (LDL-C) may protect against gallstone disease. Whether plasma levels of LDL-C per se predict risk of gallstone disease remains unclear. We tested the hypothesis that elevated LDL-C is a causal risk factor for symptomatic gallstone...

  1. Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

    Directory of Open Access Journals (Sweden)

    Yeboah J

    2014-10-01

    Full Text Available Joseph Yeboah Heart and Vascular Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, NC, USA Abstract: The ultimate goal of cardiovascular risk prediction is to identify individuals in the population to whom the application or administration of current proven lifestyle modifications and medicinal therapies will result in reduction in cardiovascular disease events and minimal adverse effects (net benefit to society. The use of cardiovascular risk prediction tools dates back to 1976 when the Framingham coronary heart disease risk score was published. Since then a lot of novel risk markers have been identified and other cardiovascular risk prediction tools have been developed to either improve or replace the Framingham Risk Score (FRS. In 2013, the new atherosclerotic cardiovascular disease risk estimator was published by the American College of Cardiology and the American Heart Association to replace the FRS for cardiovascular risk prediction. It is too soon to know the performance of the new atherosclerotic cardiovascular disease risk estimator. The risk-benefit ratio for preventive therapy (lifestyle modifications, statin +/− aspirin based on cardiovascular disease risk assessed using the FRS is unknown but it was assumed to be a net benefit. Should we also assume the risk-benefit ratio for the new atherosclerotic cardiovascular disease risk estimator is also a net benefit? Keywords: risk prediction, prevention, cardiovascular disease

  2. Importance of Android/Gynoid Fat Ratio in Predicting Metabolic and Cardiovascular Disease Risk in Normal Weight as well as Overweight and Obese Children

    Science.gov (United States)

    Regier, Michael

    2014-01-01

    Numerous studies have shown that android or truncal obesity is associated with a risk for metabolic and cardiovascular disease, yet there is evidence that gynoid fat distribution may be protective. However, these studies have focused on adults and obese children. The purpose of our study was to determine if the android/gynoid fat ratio is positively correlated with insulin resistance, HOMA2-IR, and dislipidemia in a child sample of varying body sizes. In 7–13-year-old children with BMI percentiles ranging from 0.1 to 99.6, the android/gynoid ratio was closely associated with insulin resistance and combined LDL + VLDL-cholesterol. When separated by sex, it became clear that these relationships were stronger in boys than in girls. Subjects were stratified into BMI percentile based tertiles. For boys, the android/gynoid ratio was significantly related to insulin resistance regardless of BMI tertile with and LDL + VLDL in tertiles 1 and 3. For girls, only LDL + VLDL showed any significance with android/gynoid ratio and only in tertile 2. We conclude that the android/gynoid fat ratio is closely associated with insulin resistance and LDL + VLDL-, “bad,” cholesterol in normal weight boys and may provide a measurement of metabolic and cardiovascular disease risk in that population. PMID:25302115

  3. Importance of android/gynoid fat ratio in predicting metabolic and cardiovascular disease risk in normal weight as well as overweight and obese children.

    Science.gov (United States)

    Samsell, Lennie; Regier, Michael; Walton, Cheryl; Cottrell, Lesley

    2014-01-01

    Numerous studies have shown that android or truncal obesity is associated with a risk for metabolic and cardiovascular disease, yet there is evidence that gynoid fat distribution may be protective. However, these studies have focused on adults and obese children. The purpose of our study was to determine if the android/gynoid fat ratio is positively correlated with insulin resistance, HOMA2-IR, and dislipidemia in a child sample of varying body sizes. In 7-13-year-old children with BMI percentiles ranging from 0.1 to 99.6, the android/gynoid ratio was closely associated with insulin resistance and combined LDL + VLDL-cholesterol. When separated by sex, it became clear that these relationships were stronger in boys than in girls. Subjects were stratified into BMI percentile based tertiles. For boys, the android/gynoid ratio was significantly related to insulin resistance regardless of BMI tertile with and LDL + VLDL in tertiles 1 and 3. For girls, only LDL + VLDL showed any significance with android/gynoid ratio and only in tertile 2. We conclude that the android/gynoid fat ratio is closely associated with insulin resistance and LDL + VLDL-, "bad," cholesterol in normal weight boys and may provide a measurement of metabolic and cardiovascular disease risk in that population.

  4. Evaluation of Hs-CRP levels and interleukin 18 (-137G/C promoter polymorphism in risk prediction of coronary artery disease in first degree relatives.

    Directory of Open Access Journals (Sweden)

    Rajesh Kumar G

    Full Text Available Coronary Artery Disease (CAD is clearly a multifactorial disease that develops from childhood and ultimately leads to death. Several reports revealed having a First Degree Relatives (FDRS with premature CAD is a significant autonomous risk factor for CAD development. C - reactive protein (CRP is a member of the pentraxin family and is the most widely studied proinflammatory biomarker. IL-18 is a pleiotrophic and proinflammatory cytokine which is produced mainly by macrophages and plays an important role in the inflammatory cascade.Hs-CRP levels were estimated by ELISA and Genotyping of IL-18 gene variant located on promoter -137 (G/C by Allele specific PCR in blood samples of 300 CAD patients and 300 controls and 100 FDRS. Promoter Binding sites and Protein interacting partners were identified by Alibaba 2.1 and Genemania online tools respectively. Hs-CRP levels were significantly high in CAD patients followed by FDRS when compared to controls. In IL-18 -137 (G/C polymorphism homozygous GG is significantly associated with occurrence of CAD and Hs-CRP levels were significantly higher in GG genotype subjects when compared to GC and CC. IL-18 was found to be interacting with 100 protein interactants.Our results indicate that Hs-CRP levels and IL-18-137(G/C polymorphism may help to identify risk of future events of CAD in asymptomatic healthy FDRS.

  5. Evaluation of Hs-CRP levels and interleukin 18 (-137G/C) promoter polymorphism in risk prediction of coronary artery disease in first degree relatives.

    Science.gov (United States)

    G, Rajesh Kumar; K, Mrudula Spurthi; G, Kishore Kumar; Kurapati, Mohanalatha; M, Saraswati; T, Mohini Aiyengar; P, Chiranjeevi; G, Srilatha Reddy; S, Nivas; P, Kaushik; K, Sanjib Sahu; H, Surekha Rani

    2015-01-01

    Coronary Artery Disease (CAD) is clearly a multifactorial disease that develops from childhood and ultimately leads to death. Several reports revealed having a First Degree Relatives (FDRS) with premature CAD is a significant autonomous risk factor for CAD development. C - reactive protein (CRP) is a member of the pentraxin family and is the most widely studied proinflammatory biomarker. IL-18 is a pleiotrophic and proinflammatory cytokine which is produced mainly by macrophages and plays an important role in the inflammatory cascade. Hs-CRP levels were estimated by ELISA and Genotyping of IL-18 gene variant located on promoter -137 (G/C) by Allele specific PCR in blood samples of 300 CAD patients and 300 controls and 100 FDRS. Promoter Binding sites and Protein interacting partners were identified by Alibaba 2.1 and Genemania online tools respectively. Hs-CRP levels were significantly high in CAD patients followed by FDRS when compared to controls. In IL-18 -137 (G/C) polymorphism homozygous GG is significantly associated with occurrence of CAD and Hs-CRP levels were significantly higher in GG genotype subjects when compared to GC and CC. IL-18 was found to be interacting with 100 protein interactants. Our results indicate that Hs-CRP levels and IL-18-137(G/C) polymorphism may help to identify risk of future events of CAD in asymptomatic healthy FDRS.

  6. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    NARCIS (Netherlands)

    Schalkwijk, D.B. van; Graaf, A.A. de; Tsivtsivadze, E.; Parnell, L.D.; Werff-van der Vat, B.J.C. van der; Ommen, B. van; Greef, J. van der; Ordovás, J.M.

    2014-01-01

    Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to

  7. Dentistry: risks for addictive disease.

    Science.gov (United States)

    Walter, Jane

    2007-01-01

    Chemical dependence is chronic disease with genetic, psychosocial, and environmental contributing factors and neurological characteristics. Dentists may be at an increased risk for addiction because they are in a helping profession, work in a stressful environment in which drugs are readily available, often exhibit perfectionist personality traits, and function in isolation. Treatment can be effective, especially when provided by staff skilled in working with healthcare professionals, using the Twelve-Step approach, involving families, and addressing related dysfunctional behavior patterns and psychological issues.

  8. Predicting the effect of prevention of ischaemic heart disease

    DEFF Research Database (Denmark)

    Brønnum-Hansen, Henrik

    2002-01-01

    Priority setting in public health policy must be based on information on the effectiveness of alternative preventive and therapeutic interventions. The purpose of this study is to predict the effect on mortality from ischaemic heart disease (IHD) in Denmark of reduced exposure to the risk factors...... hypertension, hypercholesterolaemia, cigarette smoking, and physical inactivity....

  9. Do Physical Activity, Body Mass Index, and Sleep Duration Predict Clustered Cardiovascular Disease Risk in Children?- A Part of the OPUS Study

    DEFF Research Database (Denmark)

    Hjorth, Mads F.; Damsgaard, Camilla T.; Dalskov, Stine-Mathilde

    Objective To investigate the single and combined associations of physical activity (PA), body mass index (BMI), and sleep duration with clustering of cardiovascular disease risk markers in healthy children. Methods We did a cross-sectional pilot-study of 74 Danish school children aged 8-11 years...... BMI was 17.1 (range 13.4-25.4) kg/m2, with 10.8% classified as overweight using isoBMI. Controlled for age and sex, P A was negatively associated with cMET-score (Standardised beta coefficients (sBeta)=-0.32); n=74; P=0.016) and BMI was positively associated with cMETscore (sBeta=0.49; n=74; P...

  10. Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

    Science.gov (United States)

    Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

    2017-11-01

    Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  11. Thallium stress testing does not predict cardiovascular risk in diabetic patients with end-stage renal disease undergoing cadaveric renal transplantation

    International Nuclear Information System (INIS)

    Holley, J.L.; Fenton, R.A.; Arthur, R.S.

    1991-01-01

    This study assessed the usefulness of thallium stress testing as a predictor of perioperative cardiovascular risk in diabetic patients with end-stage renal disease undergoing cadaveric renal transplantation. Demographic factors influencing the exercise performance in these patients were also examined. The medical records of 189 consecutive patients with diabetic nephropathy who were evaluated for cadaveric renal transplantation were reviewed. Thallium stress testing was the initial examination of cardiovascular status in 141 patients. An adequate examination was one in which at least 70% of maximum heart rate was achieved. A thallium stress test was normal if there were no ST segment depressions on the electrocardiogram and no perfusion abnormalities on the thallium scan. Forty-four patients underwent cardiac catheterization as the initial evaluation (Group C) and four patients underwent transplantation without a formal cardiovascular evaluation (Group D). Sixty-four of the 141 patients undergoing thallium stress testing had an adequate and normal examination (Group A). The incidence of perioperative cardiac events in this group was 2%. Seventy-seven patients (Group B) had an abnormal (n = 41) or an inadequate (n = 36) thallium stress test and most (n = 61) then underwent coronary angiography. The use of beta-blockers was the only predictor of an abnormal or inadequate thallium stress test. Forty-three percent of patients with inadequate or abnormal thallium stress tests had significant coronary artery disease on cardiac catheterization. The perioperative risk of cardiac events was not different in Group A versus Groups B, C, and D combined. Survival of Group A and B patients was not different but was significantly longer than that of Group C patients

  12. Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention.

    Directory of Open Access Journals (Sweden)

    Mar Pujades-Rodriguez

    Full Text Available Recent experimental evidence suggests that socioeconomic characteristics of neighbourhoods influence cardiovascular health, but observational studies which examine deprivation across a wide range of cardiovascular diseases (CVDs are lacking.Record-linkage cohort study of 1.93 million people to examine the association between small-area socioeconomic deprivation and 12 CVDs. Health records covered primary care, hospital admissions, a myocardial infarction registry and cause-specific mortality in England (CALIBER. Patients were aged ≥30 years and were initially free of CVD. Cox proportional hazard models stratified by general practice were used.During a median follow-up of 5.5 years 114,859 people had one of 12 initial CVD presentations. In women the hazards of all CVDs except abdominal aortic aneurysm increased linearly with higher small-area socioeconomic deprivation (adjusted HR for most vs. least deprived ranged from 1.05, 95%CI 0.83-1.32 for abdominal aortic aneurysm to 1.55, 95%CI 1.42-1.70 for heart failure; I2 = 81.9%, τ2 = 0.01. In men heterogeneity was higher (HR ranged from 0.89, 95%CI 0.75-1.06 for cardiac arrest to 1.85, 95%CI 1.67-2.04 for peripheral arterial disease; I2 = 96.0%, τ2 = 0.06 and no association was observed with stable angina, sudden cardiac death, subarachnoid haemorrhage, transient ischaemic attack and abdominal aortic aneurysm. Lifetime risk difference between least and most deprived quintiles was most marked for peripheral arterial disease in women (4.3% least deprived, 5.8% most deprived and men (4.6% least deprived, 7.8% in most deprived; but it was small or negligible for sudden cardiac death, transient ischaemic attack, abdominal aortic aneurysm and ischaemic and intracerebral haemorrhage, in both women and men.Associations of small-area socioeconomic deprivation with 12 types of CVDs were heterogeneous, and in men absent for several diseases. Findings suggest that policies to reduce

  13. Temporal Trends in Disease Severity and Predicted Surgical Risk at the Time of Referral for Echocardiography in Patients Diagnosed with Aortic Stenosis

    DEFF Research Database (Denmark)

    Ersboll, Mads; Samad, Zainab; Al Enezi, Fawaz

    2015-01-01

    BACKGROUND: Calcific aortic stenosis (AS) is the most common underlying pathology in patients undergoing heart valve surgery, with an expected increasing prevalence among the aging population. METHODS AND RESULTS: We identified the temporal trends in referral patterns, disease severity, and assoc......BACKGROUND: Calcific aortic stenosis (AS) is the most common underlying pathology in patients undergoing heart valve surgery, with an expected increasing prevalence among the aging population. METHODS AND RESULTS: We identified the temporal trends in referral patterns, disease severity......, and associated surgical risk among patients with AS between January 1, 1995 and December 31, 2012 at the Duke University Hospital. A total of 6103 patients had a finding of mild (n = 3303), moderate (n = 1648), or severe AS (n = 1152) in a native aortic valve. Overall presence of severe AS increased...... with a finding of severe AS, the proportion of patients aged older than 80 years increased to 51.0% in the most recent time period (2010-2012) compared with 32.6% in the preceding time period (P proportion of patients with a logistic EuroSCORE greater than 20...

  14. Periodontal profile classes predict periodontal disease progression and tooth loss.

    Science.gov (United States)

    Morelli, Thiago; Moss, Kevin L; Preisser, John S; Beck, James D; Divaris, Kimon; Wu, Di; Offenbacher, Steven

    2018-02-01

    Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived

  15. Differential incremental value of ultrasound carotid intima-media thickness, carotid plaque, and cardiac calcium to predict angiographic coronary artery disease across Framingham risk score strata in the APRES multicentre study.

    Science.gov (United States)

    Gaibazzi, Nicola; Rigo, Fausto; Facchetti, Rita; Carerj, Scipione; Giannattasio, Cristina; Moreo, Antonella; Mureddu, Gian Francesco; Salvetti, Massimo; Grolla, Elisabetta; Faden, Giacomo; Cesana, Francesca; Faggiano, Pompilio

    2016-09-01

    According to recent data, more accurate selection of patients undergoing coronary angiography for suspected coronary artery disease (CAD) is needed. From the Active PREvention Study multicentre prospective study, we further analyse whether carotid intima-media thickness (cIMT), carotid plaques (cPL), and echocardiographic cardiac calcium score (eCS) have incremental discriminatory and reclassification predictive value for CAD over clinical risk score in subjects undergoing coronary angiography, specifically depending on their low, intermediate, or high class of clinical risk. In eight centres, 445 subjects without history of prior CAD but with chest pain of recent onset and/or a positive/inconclusive stress test for ischaemia prospectively underwent clinically indicated elective coronary angiography after cardiac and carotid ultrasound assessments with measurements of cIMT, cPL, and eCS. The study population was divided into subjects at low (10%), intermediate (10-20%), and high (>20%) Framingham risk score (FRS). Ultrasound parameters were tested for their incremental value to predict CAD over FRS, in each pre-test risk category. No significant difference could be appreciated between the discrimination value of FRS and Diagnostic Imaging for Coronary Artery Disease score for the presence of CAD. eCS or cPL demonstrated significant incremental prediction over FRS, consistently in the three FRS categories (P risk subjects, in whom cPL was apparently not incremental over FRS, and eCS was only of borderline significance for better discrimination. Ultrasound eCS and cPL assessments were significant predictors of angiographic CAD in patients without prior CAD but with signs or symptoms suspect for CAD, independently and incrementally to FRS, across all pre-test risk probability strata, although in high-risk subjects, only eCS maintained an incremental value. The use of cIMT was not significantly incrementally useful in any FRS risk category. Published on behalf of the

  16. Is the disease course predictable in inflammatory bowel diseases?

    Science.gov (United States)

    Lakatos, Peter Laszlo; Kiss, Lajos S

    2010-01-01

    During the course of the disease, most patients with Crohn’s disease (CD) may eventually develop a stricturing or a perforating complication, and a significant number of patients with both CD and ulcerative colitis will undergo surgery. In recent years, research has focused on the determination of factors important in the prediction of disease course in inflammatory bowel diseases to improve stratification of patients, identify individual patient profiles, including clinical, laboratory and molecular markers, which hopefully will allow physicians to choose the most appropriate management in terms of therapy and intensity of follow-up. This review summarizes the available evidence on clinical, endoscopic variables and biomarkers in the prediction of short and long-term outcome in patients with inflammatory bowel diseases. PMID:20518079

  17. Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families

    OpenAIRE

    Muranen, Taru A.; Mavaddat, Nasim; Khan, Sofia; Fagerholm, Rainer; Pelttari, Liisa; Lee, Andrew; Aittom?ki, Kristiina; Blomqvist, Carl; Easton, Douglas F.; Nevanlinna, Heli

    2016-01-01

    The risk of developing breast cancer is increased in women with family history of breast cancer and particularly in families with multiple cases of breast or ovarian cancer. Nevertheless, many women with a positive family history never develop the disease. Polygenic risk scores (PRSs) based on the risk effects of multiple common genetic variants have been proposed for individual risk assessment on a population level. We investigate the applicability of the PRS for risk prediction within breas...

  18. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. THE ROLE OF RISK AVERSION IN PREDICTING INDIVIDUAL BEHAVIOR

    OpenAIRE

    Luigi Guiso; Monica Paiella

    2005-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers� decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways co...

  20. Subclinical organ damage and cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Olsen, Michael H

    2010-01-01

    Traditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided...... by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii...

  1. [Impact of plasma pro-B-type natriuretic peptide amino-terminal and galectin-3 levels on the predictive capacity of the LIPID Clinical Risk Scale in stable coronary disease].

    Science.gov (United States)

    Higueras, Javier; Martín-Ventura, José Luis; Blanco-Colio, Luis; Cristóbal, Carmen; Tarín, Nieves; Huelmos, Ana; Alonso, Joaquín; Pello, Ana; Aceña, Álvaro; Carda, Rocío; Lorenzo, Óscar; Mahíllo-Fernández, Ignacio; Asensio, Dolores; Almeida, Pedro; Rodríguez-Artalejo, Fernando; Farré, Jerónimo; López Bescós, Lorenzo; Egido, Jesús; Tuñón, José

    2015-01-01

    At present, there is no tool validated by scientific societies for risk stratification of patients with stable coronary artery disease (SCAD). It has been shown that plasma levels of monocyte chemoattractant protein-1 (MCP-1), galectin-3 and pro-B-type natriuretic peptide amino-terminal (NT-proBNP) have prognostic value in this population. To analyze the prognostic value of a clinical risk scale published in Long-term Intervention with Pravastatin in Ischemic Disease (LIPID) study and determining its predictive capacity when combined with plasma levels of MCP-1, galectin-3 and NT-proBNP in patients with SCAD. A total of 706 patients with SCAD and a history of acute coronary syndrome (ACS) were analyzed over a follow up period of 2.2 ± 0.99 years. The primary endpoint was the occurrence of an ischemic event (any SCA, stroke or transient ischemic attack), heart failure, or death. A clinical risk scale derived from the LIPID study significantly predicted the development of the primary endpoint, with an area under the ROC curve (Receiver Operating Characteristic) of 0.642 (0.579 to 0.705); Pvalue improved with an area under the curve of 0.744 (0.684 to 0.805); P<0.001 (P=0.022 for comparison). A score greater than 21.5 had a sensitivity of 74% and a specificity of 61% for the development of the primary endpoint (P<0.001, log -rank test). Plasma levels of MCP-1, galectin -3 and NT-proBNP improve the ability of the LIPID clinical scale to predict the prognosis of patients with SCAD. Copyright © 2014 Sociedad Española de Arteriosclerosis. Published by Elsevier España. All rights reserved.

  2. Risk stratification of patients suspected of coronary artery disease

    DEFF Research Database (Denmark)

    Jensen, Jesper M; Voss, Mette; Hansen, Vibeke Bøgelund

    2012-01-01

    To compare the performance of five risk models (Diamond-Forrester, the updated Diamond-Forrester, Morise, Duke, and a new model designated COronary Risk SCORE (CORSCORE) in predicting significant coronary artery disease (CAD) in patients with chest pain suggestive of stable angina pectoris....

  3. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  4. Total cardiovascular disease risk assessment: a review.

    LENUS (Irish Health Repository)

    Cooney, Marie Therese

    2011-09-01

    The high risk strategy for the prevention of cardiovascular disease (CVD) requires an assessment of an individual\\'s total CVD risk so that the most intensive risk factor management can be directed towards those at highest risk. Here we review developments in the assessment and estimation of total CVD risk.

  5. Predicting global variation in infectious disease severity

    DEFF Research Database (Denmark)

    Jensen, Per Moestrup; de Fine Licht, Henrik Hjarvard

    2016-01-01

    demographic and population data. Results: Birth rates were the best predictor for mumps and malaria CFR. For tuberculosis CFR death rates were the best predictor and for leptospirosis population density was a significant predictor. Conclusions and implications: CFR predictors differed among diseases according...... and leptospirosis and assessed these for association with a range of population characteristics, such as crude birth and death rates, median age of the population, mean body mass index, proportion living in urban areas and tuberculosis vaccine coverage. We then tested this predictive model on Danish his- torical...... have the opposite effect. Accordingly changes in CFR may occur in parallel with demographic transitions. Methodology: We explored the predictability of CFR using data obtained from the World Health Organization (WHO) disease databases for four human diseases: mumps, malaria, tuberculosis...

  6. Cardiovascular risk prediction tools for populations in Asia.

    Science.gov (United States)

    Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

    2007-02-01

    Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

  7. At Risk for Kidney Disease?

    Science.gov (United States)

    ... Heart Disease Mineral & Bone Disorder Causes of Chronic Kidney Disease Diabetes and high blood pressure are the most ... blood vessels in your kidneys. Other causes of kidney disease Other causes of kidney disease include a genetic ...

  8. DHA-enriched high–oleic acid canola oil improves lipid profile and lowers predicted cardiovascular disease risk in the canola oil multicenter randomized controlled trial123

    Science.gov (United States)

    Jones, Peter JH; Senanayake, Vijitha K; Pu, Shuaihua; Jenkins, David JA; Connelly, Philip W; Lamarche, Benoît; Couture, Patrick; Charest, Amélie; Baril-Gravel, Lisa; West, Sheila G; Liu, Xiaoran; Fleming, Jennifer A; McCrea, Cindy E; Kris-Etherton, Penny M

    2014-01-01

    Background: It is well recognized that amounts of trans and saturated fats should be minimized in Western diets; however, considerable debate remains regarding optimal amounts of dietary n−9, n−6, and n−3 fatty acids. Objective: The objective was to examine the effects of varying n−9, n−6, and longer-chain n−3 fatty acid composition on markers of coronary heart disease (CHD) risk. Design: A randomized, double-blind, 5-period, crossover design was used. Each 4-wk treatment period was separated by 4-wk washout intervals. Volunteers with abdominal obesity consumed each of 5 identical weight-maintaining, fixed-composition diets with one of the following treatment oils (60 g/3000 kcal) in beverages: 1) conventional canola oil (Canola; n−9 rich), 2) high–oleic acid canola oil with docosahexaenoic acid (CanolaDHA; n−9 and n−3 rich), 3) a blend of corn and safflower oil (25:75) (CornSaff; n−6 rich), 4) a blend of flax and safflower oils (60:40) (FlaxSaff; n−6 and short-chain n−3 rich), or 5) high–oleic acid canola oil (CanolaOleic; highest in n−9). Results: One hundred thirty individuals completed the trial. At endpoint, total cholesterol (TC) was lowest after the FlaxSaff phase (P < 0.05 compared with Canola and CanolaDHA) and highest after the CanolaDHA phase (P < 0.05 compared with CornSaff, FlaxSaff, and CanolaOleic). Low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol were highest, and triglycerides were lowest, after CanolaDHA (P < 0.05 compared with the other diets). All diets decreased TC and LDL cholesterol from baseline to treatment endpoint (P < 0.05). CanolaDHA was the only diet that increased HDL cholesterol from baseline (3.5 ± 1.8%; P < 0.05) and produced the greatest reduction in triglycerides (−20.7 ± 3.8%; P < 0.001) and in systolic blood pressure (−3.3 ± 0.8%; P < 0.001) compared with the other diets (P < 0.05). Percentage reductions in Framingham 10-y CHD risk scores (FRS) from

  9. Risk prediction of hepatotoxicity in paracetamol poisoning.

    Science.gov (United States)

    Wong, Anselm; Graudins, Andis

    2017-09-01

    Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

  10. [Predictive ocular motor control in Parkinson's disease].

    Science.gov (United States)

    Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

    2008-02-19

    To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

  11. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

    Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

  12. Insulin Resistance and Risk of Cardiovascular Disease in Postmenopausal Women

    DEFF Research Database (Denmark)

    Schmiegelow, Michelle D; Hedlin, Haley; Stefanick, Marcia L

    2015-01-01

    BACKGROUND: Insulin resistance is associated with diabetes mellitus, but it is uncertain whether it improves cardiovascular disease (CVD) risk prediction beyond traditional cardiovascular risk factors. METHODS AND RESULTS: We identified 15,288 women from the Women's Health Initiative Biomarkers....../HDL-C, or impaired fasting glucose (serum glucose ≥110 mg/dL) to traditional risk factors in separate Cox multivariable analyses and assessed risk discrimination and reclassification. The study end point was major CVD events (nonfatal and fatal coronary heart disease and ischemic stroke) within 10 years, which...

  13. A risk prediction model for xerostomia: a retrospective cohort study.

    Science.gov (United States)

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  14. Shoulder dystocia: risk factors, predictability, and preventability.

    Science.gov (United States)

    Mehta, Shobha H; Sokol, Robert J

    2014-06-01

    Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Predicting cognitive decline in Alzheimer's disease: an integrated analysis

    DEFF Research Database (Denmark)

    Lopez, Oscar L; Schwam, Elias; Cummings, Jeffrey

    2010-01-01

    Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined.......Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined....

  16. New methods for fall risk prediction.

    Science.gov (United States)

    Ejupi, Andreas; Lord, Stephen R; Delbaere, Kim

    2014-09-01

    Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

  17. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  18. The prediction of the bankruptcy risk

    Directory of Open Access Journals (Sweden)

    Gheorghe DUMITRESCU

    2010-04-01

    Full Text Available The study research results of the bankruptcy risk in the actual economic crisis are very weak. This issue is very important for the economy of every country, no matter what their actual development level.The necessity of bankruptcy risk prediction appears in every company,but also in the related institutions like financial companies, investors, suppliers, customers.The bankruptcy risk made and makes the object of many studies of research that want to identify: the moment of the appearance of the bankruptcy, the factors that compete at the reach of this state, the indicators that express the best this orientation (to the bankruptcy.The threats to the firms impose the knowledge by the managers,permanently of the economic-financial situations, of the vulnerable areas and of those with potential of development. Thus, these must identify and gesture the threats that would stop the fulfillment of the established purposes.

  19. Predictive gene testing for Huntington disease and other neurodegenerative disorders.

    Science.gov (United States)

    Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

    2013-12-01

    Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

  20. A Comparison of the Updated Diamond-Forrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: The SCOT-HEART Coronary CTA Cohort.

    Science.gov (United States)

    Baskaran, Lohendran; Danad, Ibrahim; Gransar, Heidi; Ó Hartaigh, Bríain; Schulman-Marcus, Joshua; Lin, Fay Y; Peña, Jessica M; Hunter, Amanda; Newby, David E; Adamson, Philip D; Min, James K

    2018-04-13

    This study sought to compare the performance of history-based risk scores in predicting obstructive coronary artery disease (CAD) among patients with stable chest pain from the SCOT-HEART study. Risk scores for estimating pre-test probability of CAD are derived from referral-based populations with a high prevalence of disease. The generalizability of these scores to lower prevalence populations in the initial patient encounter for chest pain is uncertain. We compared 3 scores among patients with suspected CAD in the coronary computed tomographic angiography (CTA) randomized arm of the SCOT-HEART study for the outcome of obstructive CAD by coronary CTA: the updated Diamond-Forrester score (UDF), CAD Consortium clinical score (CAD2), and CONFIRM risk score (CRS). We tested calibration with goodness-of-fit, discrimination with area under the receiver-operating curve (AUC), and reclassification with net reclassification improvement (NRI) to identify low-risk patients. In 1,738 patients (58 ± 10 years and 44.0% women), overall calibration was best for UDF, with underestimation by CRS and CAD2. Discrimination by AUC was highest for CAD2 at 0.79 (95% confidence interval [CI]: 0.77 to 0.81) than for UDF (0.77 [95% CI: 0.74 to 0.79]) or CRS (0.75 [95% CI: 0.73 to 0.77]) (p CAD2 (NRI 0.31, 95% CI: 0.27 to 0.35) followed by CRS (NRI 0.21, 95% CI: 0.17 to 0.25) compared with UDF (p CAD and uniform CAD evaluation by coronary CTA, CAD2 provided the best discrimination and classification, despite overestimation of obstructive CAD as evaluated by coronary CTA. CRS exhibited intermediate performance followed by UDF for discrimination and reclassification. Copyright © 2018. Published by Elsevier Inc.

  1. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  2. Borrelia infection and risk of celiac disease.

    Science.gov (United States)

    Alaedini, Armin; Lebwohl, Benjamin; Wormser, Gary P; Green, Peter H; Ludvigsson, Jonas F

    2017-09-15

    Environmental factors, including infectious agents, are speculated to play a role in the rising prevalence and the geographic distribution of celiac disease, an autoimmune disorder. In the USA and Sweden where the regional variation in the frequency of celiac disease has been studied, a similarity with the geographic distribution of Lyme disease, an emerging multisystemic infection caused by Borrelia burgdorferi spirochetes, has been found, thus raising the possibility of a link. We aimed to determine if infection with Borrelia contributes to an increased risk of celiac disease. Biopsy reports from all of Sweden's pathology departments were used to identify 15,769 individuals with celiac disease. Through linkage to the nationwide Patient Register, we compared the rate of earlier occurrence of Lyme disease in the patients with celiac disease to that in 78,331 matched controls. To further assess the temporal relationship between Borrelia infection and celiac disease, we also examined the risk of subsequent Lyme disease in patients with a diagnosis of celiac disease. Twenty-five individuals (0.16%) with celiac disease had a prior diagnosis of Lyme disease, whereas 79 (0.5%) had a subsequent diagnosis of Lyme disease. A modest association between Lyme disease and celiac disease was seen both before (odds ratio, 1.61; 95% confidence interval (CI), 1.06-2.47) and after the diagnosis of celiac disease (hazard ratio, 1.82; 95% CI, 1.40-2.35), with the risk of disease being highest in the first year of follow-up. Only a minor fraction of the celiac disease patient population had a prior diagnosis of Lyme disease. The similar association between Lyme disease and celiac disease both before and after the diagnosis of celiac disease is strongly suggestive of surveillance bias as a likely contributor. Taken together, the data indicate that Borrelia infection is not a substantive risk factor in the development of celiac disease.

  3. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  4. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  5. Lung cancer in never smokers Epidemiology and risk prediction models

    Science.gov (United States)

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  6. Divorce and risk of hospital-diagnosed infectious diseases.

    Science.gov (United States)

    Nielsen, Nete Munk; Davidsen, Rie B; Hviid, Anders; Wohlfahrt, Jan

    2014-11-01

    Although, divorce is considered to have a negative impact on morbidity, very little is known concerning exposure to divorce and risk of infectious diseases. We aimed to investigate the association between divorce and subsequent hospital contacts with infectious diseases. We performed a nation-wide cohort study, including all Danish men and women (n≈5.6 million) alive on the 1 January 1982 or later, and followed them for infectious disease diagnosed in hospital settings from 1982 to 2010. The association between divorce and risk of infectious diseases was evaluated through rate ratios (RRs) comparing incidence rates of infectious diseases between divorced and married pesons. Compared with married persons, divorced persons were overall at a 1.48 fold (RR=1.48 (95% CI: 1.47-1.50)) increased risk of hospital-diagnosed infectious diseases (RR adjusted for sex, age, period, income and education). The risk of infectious diseases was slightly more pronounced for divorced women (RR=1.54 (1.52-1.56)) than divorced men ((RR=1.42 (1.41-1.44)). The increased risk remained almost unchanged even more than 15 years after the divorce. Young age at divorce, short duration of marriage and number of divorces further increased the risk of infectious diseases, whereas number of children at time of divorce had no impact on risk of hospital-diagnosed infectious diseases following the divorce. Divorce appears to have a moderate but long lasting impact on the risk of infectious diseases the underlying mechanism is unknown but shared risk factors predicting divorce and infectious diseases could contribute to our findings. © 2014 the Nordic Societies of Public Health.

  7. Be-CoDiS: A Mathematical Model to Predict the Risk of Human Diseases Spread Between Countries--Validation and Application to the 2014-2015 Ebola Virus Disease Epidemic.

    Science.gov (United States)

    Ivorra, Benjamin; Ngom, Diène; Ramos, Ángel M

    2015-09-01

    Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the UK, the USA and Spain. Regarding the emergency of this situation, there is a need for the development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Between-Countries Disease Spread (Be-CoDiS), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time-dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-2015 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other model outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free MATLAB version of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm.

  8. Prediction of tension-type headache risk in adolescents

    Directory of Open Access Journals (Sweden)

    K. A. Stepanchenko

    2016-08-01

    Full Text Available Tension-type headache is the actual problem of adolescent neurology, which is associated with the prevalence of the disease, the tendency of the disease to the chronic course and a negative impact on performance in education, work capacity and quality of patients’ life. The aim. To develop a method for prediction of tension-type headache occurrence in adolescents. Materials and methods. 2342 adolescent boys and girls at the age of 13-17 years in schools of Kharkiv were examined. We used questionnaire to identify the headache. A group of adolescents with tension-type headache - 1430 people (61.1% was selected. The control group included 246 healthy adolescents. Possible risk factors for tension-type headache formation were divided into 4 groups: genetic, biomedical, psychosocial and social. Mathematical prediction of tension-type headache risk in adolescents was performed using the method of intensive indicators normalization of E.N. Shigan, which was based on probabilistic Bayesian’s method. The result was presented in the form of prognostic coefficients. Results. The most informative risk factors for tension-type headache development were the diseases, from which the teenager suffered after 1 year (sleep disorders, gastrointestinal diseases, autonomic disorders in the family history, traumatic brain injury, physical inactivity, poor adaptation of the patient in the kindergarten and school, stresses. Diagnostic scale has been developed to predict the risk of tension-type headache. It includes 23 prognostic factors with their gradation and meaning of integrated risk indicator, depending on individual factor strength influence. The risk of tension-type headache development ranged from 25,27 to 81,43 values of prognostic coefficient (low probability (25,27-43,99, the average probability (43,99-62,71 and high probability (62,71- 81,43. Conclusion. The study of tension-type headache risk factors, which were obtained by using an assessed and

  9. New technologies in predicting, preventing and controlling emerging infectious diseases.

    Science.gov (United States)

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

  10. Worldwide risks of animal diseases: introduction.

    Science.gov (United States)

    Pearson, J E

    2006-01-01

    Animal diseases impact food supplies, trade and commerce, and human health and well-being in every part of the world. Outbreaks draw the attention of those in agriculture, regulatory agencies, and government, as well as the general public. This was demonstrated by the 2000-2001 foot and mouth disease (FMD) outbreaks that occurred in Europe, South America, Asia and Africa and by the recent increased occurrence of emerging diseases transmitted from animals to humans. Examples of these emerging zoonotic diseases are highly pathogenic avian influenza, bovine spongiform encephalopathy, West Nile virus and severe acute respiratory syndrome. There is also the risk of well-known and preventable zoonotic diseases, such as rabies, brucellosis, leishmaniasis, and echinococcosis/hydatidosis, in certain countries; these diseases have a high morbidity with the potential for a very high mortality. Animal agriculturalists should have a global disease awareness of disease risks and develop plans of action to deal with them; in order to better respond to these diseases, they should develop the skills and competencies in politics, media interactions, and community engagement. This issue of Veterinaria Italiana presents information on the risk of animal diseases; their impact on animals and humans at the international, national, industry, and societal levels; and the responses to them. In addition, specific information is provided on national and international disease monitoring, surveillance and reporting, the risk of spread of disease by bioterrorism and on import risk analysis.

  11. Can we Predict Disease Course with Clinical Factors?

    Science.gov (United States)

    Vegh, Zsuzsanna; Kurti, Zsuzsanna; Golovics, Petra A; Lakatos, Peter L

    2018-01-01

    The disease phenotype at diagnosis and the disease course of Crohn's disease (CD) and ulcerative colitis (UC) show remarkable heterogeneity across patients. This review aims to summarize the currently available evidence on clinical and some environmental predictive factors, which clinicians should evaluate in the everyday practice together with other laboratory and imaging data to prevent disease progression, enable a more personalized therapy, and avoid negative disease outcomes. In recent population-based epidemiological and referral cohort studies, the evolution of disease phenotype of CD and UC varied significantly. Most CD and severe UC patients still require hospitalization or surgery/colectomy during follow-up. A change in the natural history of inflammatory bowel diseases (IBD) with improved outcomes in parallel with tailored positioning of aggressive immunomodulator and biological therapy has been suspected. According to the currently available literature, it is of major importance to refer IBD cases at risk for adverse disease outcomes as early during the disease course as possible. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  12. Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning

    OpenAIRE

    Tsui, Nancy B. Y.; Cheng, Gregory; Chung, Teresa; Lam, Christopher W. K.; Yee, Anita; Chung, Peter K. C.; Kwan, Tsz-Ki; Ko, Elaine; He, Daihai; Wong, Wing-Tak; Lau, Johnson Y. N.; Lau, Lok Ting; Fok, Manson

    2018-01-01

    The genetic bases of many common diseases have been identified through genome-wide association studies in the past decade. However, the application of this approach on public healthcare planning has not been well established. Using Macau with population of around 650,000 as a basis, we conducted a pilot study to evaluate the feasibility of population genomic research and its potential on public health decisions. By performing genome-wide SNP genotyping of over a thousand Macau individuals, we...

  13. The potential of large studies for building genetic risk prediction models

    Science.gov (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  14. Prediction of disease course in inflammatory bowel diseases.

    Science.gov (United States)

    Lakatos, Peter Laszlo

    2010-06-07

    Clinical presentation at diagnosis and disease course of both Crohn's disease (CD) and ulcerative colitis are heterogeneous and variable over time. Since most patients have a relapsing course and most CD patients develop complications (e.g. stricture and/or perforation), much emphasis has been placed in the recent years on the determination of important predictive factors. The identification of these factors may eventually lead to a more personalized, tailored therapy. In this TOPIC HIGHLIGHT series, we provide an update on the available literature regarding important clinical, endoscopic, fecal, serological/routine laboratory and genetic factors. Our aim is to assist clinicians in the everyday practical decision-making when choosing the treatment strategy for their patients suffering from inflammatory bowel diseases.

  15. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Science.gov (United States)

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  16. Diet and risk of inflammatory bowel disease

    DEFF Research Database (Denmark)

    Andersen, Vibeke; Olsen, Anja; Carbonnel, Franck

    2012-01-01

    Background: A better understanding of the environmental factors leading to inflammatory bowel disease should help to prevent occurrence of the disease and its relapses. Aim: To review current knowledge on dietary risk factors for inflammatory bowel disease. Methods: The PubMed, Medline and Cochrane...... Library were searched for studies on diet and risk of inflammatory bowel disease. Results: Established non-diet risk factors include family predisposition, smoking, appendectomy, and antibiotics. Retrospective case–control studies are encumbered with methodological problems. Prospective studies...... on European cohorts, mainly including middle-aged adults, suggest that a diet high in protein from meat and fish is associated with a higher risk of inflammatory bowel disease. Intake of the n-6 polyunsaturated fatty acid linoleic acid may confer risk of ulcerative colitis, whereas n-3 polyunsaturated fatty...

  17. Predictions of space radiation fatality risk for exploration missions.

    Science.gov (United States)

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  18. Turning 18 with congenital heart disease: prediction of infective endocarditis based on a large population

    NARCIS (Netherlands)

    Verheugt, Carianne L.; Uiterwaal, Cuno S. P. M.; van der Velde, Enno T.; Meijboom, Folkert J.; Pieper, Petronella G.; Veen, Gerrit; Stappers, Jan L. M.; Grobbee, Diederick E.; Mulder, Barbara J. M.

    2011-01-01

    The risk of infective endocarditis (IE) in adults with congenital heart disease is known to be increased, yet empirical risk estimates are lacking. We sought to predict the occurrence of IE in patients with congenital heart disease at the transition from childhood into adulthood. We identified

  19. Fungal Diseases: Ringworm Risk & Prevention

    Science.gov (United States)

    ... Testing Treatment & Outcomes Health Professionals Statistics More Resources Candidiasis Candida infections of the mouth, throat, and esophagus Vaginal candidiasis Invasive candidiasis Definition Symptoms Risk & Prevention Sources Diagnosis ...

  20. Risk based surveillance for vector borne diseases

    DEFF Research Database (Denmark)

    Bødker, Rene

    of samples and hence early detection of outbreaks. Models for vector borne diseases in Denmark have demonstrated dramatic variation in outbreak risk during the season and between years. The Danish VetMap project aims to make these risk based surveillance estimates available on the veterinarians smart phones...... in Northern Europe. This model approach may be used as a basis for risk based surveillance. In risk based surveillance limited resources for surveillance are targeted at geographical areas most at risk and only when the risk is high. This makes risk based surveillance a cost effective alternative...... sample to a diagnostic laboratory. Risk based surveillance models may reduce this delay. An important feature of risk based surveillance models is their ability to continuously communicate the level of risk to veterinarians and hence increase awareness when risk is high. This is essential for submission...

  1. Spatial Analysis of West Nile Virus: Predictive Risk Modeling of a Vector-borne Infectious Disease in Illinois by Means of NASA Earth Observation Systems

    Science.gov (United States)

    Renneboog, Nathan; Gathings, David; Hemmings, Sarah; Makasa, Emmanuel; Omer, Wigdan; Tipre, Meghan; Wright, Catherine; McAllister, Marilyn; Luvall, Jeffrey C.

    2009-01-01

    West Nile Virus is a mosquito-borne virus of the family Flaviviridae. It infects birds and various mammals, including humans, and can cause encephalitis that may prove fatal, notably among vulnerable populations. Since its identification in New York City in 1999, WNV has become established in a broad range of ecological settings throughout North America, infecting more than 25,300 people and killing 1133 as of 2008 (CDC,2009). WNV is transmitted by mosquitoes that feed on infected birds. As a result, the degree of human infection depends on local ecology and human exposure. This study hypothesizes that remote sensing and GIS can be used to analyze environmental determinants of WNV transmission, such as climate, elevation, land cover, and vegetation densities, to map areas of WNV risk for surveillance and intervention.

  2. Individualized Vascular Disease Prevention in High-Risk Patients

    NARCIS (Netherlands)

    Kaasenbrood, L

    2016-01-01

    In the pharmacologic prevention of vascular events, clinicians need to translate average effects from a clinical trial to the individual patient. Prediction models can contribute to individualized vascular disease prevention by selecting patients for treatment based on estimated risk or expected

  3. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

    Science.gov (United States)

    Parrinello, C M; Matsushita, K; Woodward, M; Wagenknecht, L E; Coresh, J; Selvin, E

    2016-09-01

    To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications. © 2016 John Wiley & Sons Ltd.

  4. Vitamin D, cardiovascular disease and risk factors

    DEFF Research Database (Denmark)

    Skaaby, Tea; Thuesen, Betina H.; Linneberg, Allan

    2017-01-01

    of vitamin D effects from a cardiovascular health perspective. It focuses on vitamin D in relation to cardiovascular disease, i.e. ischemic heart disease, and stroke; the traditional cardiovascular risk factors hypertension, abnormal blood lipids, obesity; and the emerging risk factors hyperparathyroidism......, microalbuminuria, chronic obstructive pulmonary diseases, and non-alcoholic fatty liver disease. Meta-analyses of observational studies have largely found vitamin D levels to be inversely associated with cardiovascular risk and disease. However, Mendelian randomization studies and randomized, controlled trials...... (RCTs) have not been able to consistently replicate the observational findings. Several RCTs are ongoing, and the results from these are needed to clarify whether vitamin D deficiency is a causal and reversible factor to prevent cardiovascular disease....

  5. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    Science.gov (United States)

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  6. Cardiovascular risk factors and disease in women.

    Science.gov (United States)

    Gill, Sharon K

    2015-05-01

    Coronary artery disease and stroke predominantly affect older women as opposed to younger women, but the risk factors that contribute to atherosclerotic cardiovascular disease risk often start in young women. Young women with polycystic ovary syndrome (PCOS), with migraine, and who use oral contraceptive pills (OCPs) have short-term increases in thrombotic complications that can result in coronary events or stroke. Attention should be focused on risk reduction in women of all ages. Screening for and discussing diabetes, hypertension, obesity, smoking, migraine, PCOS, and pregnancy complication history and discussing the pros and cons of hormone and statin medications are part of reducing cardiovascular risk for women. Published by Elsevier Inc.

  7. Cardiovascular disease risk factors and cognitive impairment.

    Science.gov (United States)

    Nash, David T; Fillit, Howard

    2006-04-15

    The role of cardiovascular disease risk factors in the occurrence and progression of cognitive impairment has been the subject of a significant number of publications but has not achieved widespread recognition among many physicians and educated laymen. It is apparent that the active treatment of certain of these cardiovascular disease risk factors is accompanied by a reduced risk for cognitive impairment. Patients with hypertension who are treated experience fewer cardiovascular disease events as well as less cognitive impairment than similar untreated patients. Patients who exercise may present with less cognitive impairment, and obesity may increase the risk for cognitive impairment. Lipid abnormalities and genetic markers are associated with an increased risk for cardiovascular disease and cognitive impairment. Autopsy studies have demonstrated a correlation between elevated levels of cholesterol and amyloid deposition in the brain. Research has demonstrated a relation between atherosclerotic obstruction lesions in the circle of Willis and dementia. Diabetes mellitus is associated with an increased risk for cardiovascular disease and cognitive impairment. A number of nonpharmacologic factors have a role in reducing the risk for cognitive impairment. Antioxidants, fatty acids, and micronutrients may have a role, and diets rich in fruits and vegetables and other dietary approaches may improve the outlook for patients considered at risk for cognitive impairment.

  8. CHILDHOOD RISK FACTORS PREDICT CARDIOVASCULAR DISEASE, IMPAIRED FASTING GLUCOSE PLUS TYPE 2 DIABETES MELLITUS, AND HIGH BLOOD PRESSURE 26 YEARS LATER AT MEAN AGE 38: THE PRINCETON-LRC FOLLOW-UP STUDY

    Science.gov (United States)

    Morrison, John A; Glueck, Charles J.; Wang, Ping

    2012-01-01

    Objective Assess whether pediatric risk factors predict cardiovascular disease (CVD), impaired fasting glucose (IFG) + type 2 diabetes (T2DM), and high blood pressure (HBP) in young adulthood. Materials/Methods Prospective follow-up of 909 public-parochial suburban schoolchildren first studied at ages 6–18 and 26 years later at mean age 38. Pediatric triglycerides (TG), blood pressure, LDL cholesterol (LDLC), BMI, and glucose above and HDL cholesterol (HDLC) below established pediatric cutoffs, along with race, cigarette smoking, family history of CVD, T2DM, and HBP were assessed as determinants of young adult CVD, a composite variable including IFG + T2DM, and HBP. Results By stepwise logistic regression, adult CVD (19 yes, 862 no) was associated with pediatric high TG, odds ratio (OR) 5.85, 95% confidence intervals (CI) 2.3–14.7. High TG in pediatric probands with young adult CVD was familial, and was associated with early CVD in their high TG parents. Adult IFG + T2DM (114 yes, 535 no) was associated with parental T2DM (OR 2.2, 95% CI 1.38–3.6), high childhood glucose (OR 4.43, 95% CI 2–9.7), and childhood cigarette smoking (OR 1.64, 95% CI 1.03–2.61). Adult HBP (133 yes, 475 no) was associated with pediatric high BMI (OR 2.7, 95% CI 1.7–4.3) and HBP (OR=2.5, 95% CI 1.5–4.3). Conclusions Pediatric risk factors are significantly, independently related to young adult CVD, IFG+T2DM, and HBP. Identification of pediatric risk factors for CVD, IFG+T2DM, and HBP facilitates initiation of primary prevention programs to reduce development of adult CVD, IFG+T2DM, and HBP. PMID:22001337

  9. Insignificant disease among men with intermediate-risk prostate cancer.

    Science.gov (United States)

    Hong, Sung Kyu; Vertosick, Emily; Sjoberg, Daniel D; Scardino, Peter T; Eastham, James A

    2014-12-01

    A paucity of data exists on the insignificant disease potentially suitable for active surveillance (AS) among men with intermediate-risk prostate cancer (PCa). We tried to identify pathologically insignificant disease and its preoperative predictors in men who underwent radical prostatectomy (RP) for intermediate-risk PCa. We analyzed data of 1,630 men who underwent RP for intermediate-risk disease. Total tumor volume (TTV) data were available in 332 men. We examined factors associated with classically defined pathologically insignificant cancer (organ-confined disease with TTV ≤0.5 ml with no Gleason pattern 4 or 5) and pathologically favorable cancer (organ-confined disease with no Gleason pattern 4 or 5) potentially suitable for AS. Decision curve analysis was used to assess clinical utility of a multivariable model including preoperative variables for predicting pathologically unfavorable cancer. In the entire cohort, 221 of 1,630 (13.6 %) total patients had pathologically favorable cancer. Among 332 patients with TTV data available, 26 (7.8 %) had classically defined pathologically insignificant cancer. Between threshold probabilities of 20 and 40 %, decision curve analysis demonstrated that using multivariable model to identify AS candidates would not provide any benefit over simply treating all men who have intermediate-risk disease with RP. Although a minority of patients with intermediate-risk disease may harbor pathologically favorable or insignificant cancer, currently available conventional tools are not sufficiently able to identify those patients.

  10. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    International Nuclear Information System (INIS)

    Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

    2015-01-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  11. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    Energy Technology Data Exchange (ETDEWEB)

    Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

    2015-06-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  12. Predicting epidemic risk from past temporal contact data.

    Directory of Open Access Journals (Sweden)

    Eugenio Valdano

    2015-03-01

    Full Text Available Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

  13. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

    Directory of Open Access Journals (Sweden)

    Ian C Scott

    Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

  14. Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? results from the multinational coronary ct angiography evaluation for clinical outcome: An international multicenter registry (confirm) : An international multicenter registry (confirm)

    NARCIS (Netherlands)

    A. Ahmadi (Amir); J. Leipsic (Jonathon); G.M. Feuchtner (Gudrun); H. Gransar (Heidi); Kalra, D. (Dan); R. Heo (Ran); S. Achenbach (Stephan); D. Andreini (Daniele); M. Al-Mallah (Mouaz); D.S. Berman (Daniel S.); M.J. Budoff (Matthew); F. Cademartiri (Filippo); T.Q. Callister (Tracy); H.-J. Chang (Hyuk-Jae); K. Chinnaiyan (Kavitha); B.J.W. Chow (Benjamin); R.C. Cury (Ricardo); A. Delago (Augustin); M. Gomez (Millie); M. Hadamitzky (Martin); J. Hausleiter (Jörg); N. Hindoyan (Niree); P.A. Kaufmann (Philipp); Y.-J. Kim (Yong-Jin); F.Y. Lin (Fay); E. Maffei (Erica); G. Pontone (Gianluca); G.L. Raff (Gilbert); L.J. Shaw (Leslee); T.C. Villines (Todd); A.M. Dunning (Allison M.); J.K. Min (James)

    2015-01-01

    textabstractAlthough metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD), and prognosis of patients with metabolic

  15. Preventing Cardiovascular Disease Risk Factors through Aerobic ...

    African Journals Online (AJOL)

    This paper focused on the reduction of cardiovascular disease risk factors, through aerobic exercises. The central argument here is that through exercise there is the tendency for increased strength of the heart muscles. When this is the case, what follows is a reduction in body weight and ultimately less risk on the ...

  16. Chronic obstructive pulmonary disease and cancer risk

    DEFF Research Database (Denmark)

    Kornum, Jette Brommann; Sværke, Claus; Thomsen, Reimar Wernich

    2012-01-01

    Little is known about the risk of cancer in patients with chronic obstructive pulmonary disease (COPD), including which cancer sites are most affected. We examined the short- and long-term risk of lung and extrapulmonary cancer in a nationwide cohort of COPD patients....

  17. Influenza and risk of later celiac disease

    DEFF Research Database (Denmark)

    Kårhus, Line Lund; Gunnes, Nina; Størdal, Ketil

    2018-01-01

    OBJECTIVES: Influenza has been linked to autoimmune conditions, but its relationship to subsequent celiac disease (CD) is unknown. Our primary aim was to determine the risk of CD after influenza. A secondary analysis examined the risk of CD following pandemic influenza vaccination. METHODS...

  18. Depression: risk factor for cardiovascular disease

    NARCIS (Netherlands)

    Kuehl, L.K.; Penninx, B.W.J.H.; Otte, C.

    2012-01-01

    Major depression is an independent risk factor for the development of cardiovascular disease. In patients with existing cardiovascular disease, major depression has a large impact on the quality of life and is associated with a poor course and prognosis. Potential mechanisms responsible for this

  19. Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

    Directory of Open Access Journals (Sweden)

    Nicola A. Wardrop

    2014-11-01

    Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.

  20. Credit scores, cardiovascular disease risk, and human capital.

    Science.gov (United States)

    Israel, Salomon; Caspi, Avshalom; Belsky, Daniel W; Harrington, HonaLee; Hogan, Sean; Houts, Renate; Ramrakha, Sandhya; Sanders, Seth; Poulton, Richie; Moffitt, Terrie E

    2014-12-02

    Credit scores are the most widely used instruments to assess whether or not a person is a financial risk. Credit scoring has been so successful that it has expanded beyond lending and into our everyday lives, even to inform how insurers evaluate our health. The pervasive application of credit scoring has outpaced knowledge about why credit scores are such useful indicators of individual behavior. Here we test if the same factors that lead to poor credit scores also lead to poor health. Following the Dunedin (New Zealand) Longitudinal Study cohort of 1,037 study members, we examined the association between credit scores and cardiovascular disease risk and the underlying factors that account for this association. We find that credit scores are negatively correlated with cardiovascular disease risk. Variation in household income was not sufficient to account for this association. Rather, individual differences in human capital factors—educational attainment, cognitive ability, and self-control—predicted both credit scores and cardiovascular disease risk and accounted for ∼45% of the correlation between credit scores and cardiovascular disease risk. Tracing human capital factors back to their childhood antecedents revealed that the characteristic attitudes, behaviors, and competencies children develop in their first decade of life account for a significant portion (∼22%) of the link between credit scores and cardiovascular disease risk at midlife. We discuss the implications of these findings for policy debates about data privacy, financial literacy, and early childhood interventions.

  1. Education and the risk for Alzheimers disease

    DEFF Research Database (Denmark)

    Letenneur, L; Launer, L J; Andersen, K

    2000-01-01

    The hypothesis that a low educational level increases the risk for Alzheimer's disease remains controversial. The authors studied the association of years of schooling with the risk for incident dementia and Alzheimer's disease by using pooled data from four European population-based follow......-up studies. Dementia cases were identified in a two-stage procedure that included a detailed diagnostic assessment of screen-positive subjects. Dementia and Alzheimer's disease were diagnosed by using international research criteria. Educational level was categorized by years of schooling as low (...), middle (8-11), or high (> or =12). Relative risks (95% confidence intervals) were estimated by using Poisson regression, adjusting for age, sex, study center, smoking status, and self-reported myocardial infarction and stroke. There were 493 (328) incident cases of dementia (Alzheimer's disease) and 28...

  2. Reproductive factors and Parkinson's disease risk in Danish women

    DEFF Research Database (Denmark)

    Greene, N; Lassen, C F; Rugbjerg, K

    2014-01-01

    and lifestyle factors. RESULTS: After adjusting for smoking, caffeine and alcohol use, education, age, and family Parkinson's disease history, inverse associations between Parkinson's disease and early menarche (first period at ≤11 years), oral contraceptives, high parity (≥4 children) and bilateral...... and fertile life length, age at menopause or post-menopausal hormone treatment was found. CONCLUSIONS: Reproductive factors related to women's early- to mid-reproductive lives appear to be predictive of subsequent Parkinson's disease risk whereas factors occurring later in life seem less important....

  3. Perceptions of risk: understanding cardiovascular disease

    Directory of Open Access Journals (Sweden)

    Ruth Webster

    2010-09-01

    Full Text Available Ruth Webster1, Emma Heeley21Cardiovascular Division, 2Neurological and Mental Health Division, The George Institute for International Health, Camperdown, NSW, AustraliaAbstract: Cardiovascular disease (CVD is still the leading cause of death and disability worldwide despite the availability of well-established and effective preventive options. Accurate perception of a patient’s risk by both the patient and the doctors is important as this is one of the components that determine health-related behavior. Doctors tend to not use cardiovascular (CV risk calculators and underestimate the absolute CV risk of their patients. Patients show optimistic bias when considering their own risk and consistently underestimate it. Poor patient health literacy and numeracy must be considered when thinking about this problem. Patients must possess a reasonably high level of understanding of numerical processes when doctors discuss risk, a level that is not possessed by large numbers of the population. In order to overcome this barrier, doctors need to utilize various tools including the appropriate use of visual aids to accurately communicate risk with their patients. Any intervention has been shown to be better than nothing in improving health understanding. The simple process of repeatedly conveying risk information to a patient has been shown to improve accuracy of risk perception. Doctors need to take responsibility for the accurate assessment and effective communication of CV risk in their patients in order to improve patient uptake of cardioprotective lifestyle choices and preventive medications.Keywords: risk perception, cardiovascular disease, cardioprotective lifestyle

  4. Development of an attrition risk prediction tool.

    Science.gov (United States)

    Fowler, John; Norrie, Peter

    To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

  5. Conditional predictive inference for online surveillance of spatial disease incidence

    Science.gov (United States)

    Corberán-Vallet, Ana; Lawson, Andrew B.

    2012-01-01

    This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. PMID:21898522

  6. A prospective blood RNA signature for tuberculosis disease risk

    Science.gov (United States)

    Zak, Daniel E.; Penn-Nicholson, Adam; Scriba, Thomas J.; Thompson, Ethan; Suliman, Sara; Amon, Lynn M.; Mahomed, Hassan; Erasmus, Mzwandile; Whatney, Wendy; Hussey, Gregory D.; Abrahams, Deborah; Kafaar, Fazlin; Hawkridge, Tony; Verver, Suzanne; Hughes, E. Jane; Ota, Martin; Sutherland, Jayne; Howe, Rawleigh; Dockrell, Hazel M.; Boom, W. Henry; Thiel, Bonnie; Ottenhoff, Tom H.M.; Mayanja-Kizza, Harriet; Crampin, Amelia C; Downing, Katrina; Hatherill, Mark; Valvo, Joe; Shankar, Smitha; Parida, Shreemanta K; Kaufmann, Stefan H.E.; Walzl, Gerhard; Aderem, Alan; Hanekom, Willem A.

    2016-01-01

    Background Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease may lead to interventions that impact the epidemic. Methods Healthy, M. tuberculosis infected South African adolescents were followed for 2 years; blood was collected every 6 months. A prospective signature of risk was derived from whole blood RNA-Sequencing data by comparing participants who ultimately developed active tuberculosis disease (progressors) with those who remained healthy (matched controls). After adaptation to multiplex qRT-PCR, the signature was used to predict tuberculosis disease in untouched adolescent samples and in samples from independent cohorts of South African and Gambian adult progressors and controls. The latter participants were household contacts of adults with active pulmonary tuberculosis disease. Findings Of 6,363 adolescents screened, 46 progressors and 107 matched controls were identified. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% confidence interval, 63·2–68·9) and a specificity of 80·6% (79·2–82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA-Seq and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values Bill and Melinda Gates Foundation, the National Institutes of Health, Aeras, the European Union and the South African Medical Research Council (detail at end of text). PMID:27017310

  7. Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

    Science.gov (United States)

    Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

    2015-01-01

    DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

  8. Predictive analytics for supply chain collaboration, risk management ...

    African Journals Online (AJOL)

    kirstam

    management, and (2) supply chain risk management predicted financial .... overhead costs, delivery of ever-increasing customer value, flexibility with superior ... risk exposure, relationship longevity, trust and communication are considered as.

  9. Periodontal Disease, Tooth Loss, and Cancer Risk.

    Science.gov (United States)

    Michaud, Dominique S; Fu, Zhuxuan; Shi, Jian; Chung, Mei

    2017-01-01

    Periodontal disease, which includes gingivitis and periodontitis, is highly prevalent in adults and disease severity increases with age. The relationship between periodontal disease and oral cancer has been examined for several decades, but there is increasing interest in the link between periodontal disease and overall cancer risk, with systemic inflammation serving as the main focus for biological plausibility. Numerous case-control studies have addressed the role of oral health in head and neck cancer, and several cohort studies have examined associations with other types of cancers over the past decade. For this review, we included studies that were identified from either 11 published reviews on this topic or an updated literature search on PubMed (between 2011 and July 2016). A total of 50 studies from 46 publications were included in this review. Meta-analyses were conducted on cohort and case-control studies separately when at least 4 studies could be included to determine summary estimates of the risk of cancer in relation to 1) periodontal disease or 2) tooth number (a surrogate marker of periodontal disease) with adjustment for smoking. Existing data provide support for a positive association between periodontal disease and risk of oral, lung, and pancreatic cancers; however, additional prospective studies are needed to better inform on the strength of these associations and to determine whether other cancers are associated with periodontal disease. Future studies should include sufficiently large sample sizes, improved measurements for periodontal disease, and thorough adjustment for smoking and other risk factors. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  10. Elderly fall risk prediction using static posturography

    Science.gov (United States)

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity—0.114 x Eyes Closed Vector Sum Magnitude Velocity—2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  11. Elderly fall risk prediction using static posturography.

    Science.gov (United States)

    Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan; McIlroy, William E

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  12. Elderly fall risk prediction using static posturography.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP and medial-lateral (ML center of pressure (CoP motion; AP and ML CoP root mean square distance from mean (RMS; and AP, ML, and vector sum magnitude (VSM CoP velocity were calculated. Romberg Quotients (RQ were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24, prospective all fallers (42, prospective fallers (22 single, 6 multiple, and prospective non-fallers (47. Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for

  13. Classifying PML risk with disease modifying therapies.

    Science.gov (United States)

    Berger, Joseph R

    2017-02-01

    To catalogue the risk of PML with the currently available disease modifying therapies (DMTs) for multiple sclerosis (MS). All DMTs perturb the immune system in some fashion. Natalizumab, a highly effective DMT, has been associated with a significant risk of PML. Fingolimod and dimethyl fumarate have also been unquestionably associated with a risk of PML in the MS population. Concerns about PML risk with other DMTs have arisen due to their mechanism of action and pharmacological parallel to other agents with known PML risk. A method of contextualizing PML risk for DMTs is warranted. Classification of PML risk was predicated on three criteria:: 1) whether the underlying condition being treated predisposes to PML in the absence of the drug; 2) the latency from initiation of the drug to the development of PML; and 3) the frequency with which PML is observed. Among the DMTs, natalizumab occupies a place of its own with respect to PML risk. Significantly lesser degrees of risk exist for fingolimod and dimethyl fumarate. Whether PML will be observed with other DMTs in use for MS, such as, rituximab, teriflunomide, and alemtuzumab, remains uncertain. A logical classification for stratifying DMT PML risk is important for both the physician and patient in contextualizing risk/benefit ratios. As additional experience accumulates regarding PML and the DMTs, this early effort will undoubtedly require revisiting. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. RICKETTSIAL DISEASES: RISK FOR INDONESIA

    Directory of Open Access Journals (Sweden)

    Allen L. Richards

    2012-09-01

    Full Text Available Penyakit Rickettsia bersifat endemik hampir di seluruh bagian dunia, dan begitu juga di Indonesia. Termasuk dalam penyakit-penyakit rickettsia adalah tifus epidemik, tifus murine, "scrub typhus," dan "spotted fever." Tifus epidemik, yang ditularkan kepada manusia melalui tuma pada tubuh manusia, dan dapat menyebabkan sakit berat dan kematian.   Tifus murine (tifus endemik, bersumber pada pinjal hewan, merupakan penyakit yang mirip tifus epidemik, tetapi dengan gejala-gejala yang lebih ringan dan jarang menyebabkan kematian. "Scrub typhus", merupakan penyakit yang dapat ringan sampai berat dan dapat membahayakan hidup, ditularkan kepada manusia melalui gigitan tungau yang belum dewasa yang dikenal sebagai "chigger". "Spotted fever: (demam yang disertai dengan bintik-bentik pada kulit, disebabkan karena terinfeksi oleh salah satu dari berbagai spesies rickettsia dari kelompok "spotted fever", dan ditularkan kepada manusia oleh pejamu (hospes vertebrata melalui gigitan caplak (tick yang terinfeksi. Penyakit yang disebabkan oleh organisma yang menyerupai rickettsia (rickettsia-like organism adalah: "Q fever", yaitu penyakit yang akut atau kronis yang diduga ditularkan secara alamiah akibat terhirup oleh partikel udara yang terinfeksi Coxiella burnetti sejenis bakteri yang sangat resisten terhadap upaya menonaktifkannya secara kimiawi dan fisik. Bartonellosis atau penyakit Carrion, ditemukan pada daerah dengan ketinggian sedang di Andes, Amerika Selatan. Penyakit ini ditularkan oleh lalat pasir (sand flies. "Trench fever", mirip dengan tifus epidemik, ditularkan kepada manusia oleh tuma; penyakit ini sembuh sendiri. Penyakit garutan kucing (Cat-scratch disease, disebabkan oleh infeksi Bartonella henselae di tempat gigitan atau garutan kucing rumah yang merupakan hospes. Demam sennetsu, merupakan penyakit yang dapat sembuh sendiri dan hanya ditemukan di Jepang dan Malaysia. Pengobatan dengan tetrasiklin atau kloramfenikol untuk penyakit Rickettsia

  15. Parkinson disease and Alzheimer disease: environmental risk factors.

    Science.gov (United States)

    Campdelacreu, J

    2014-01-01

    The purpose of this review is to update and summarise available evidence on environmental risk factors that have been associated with risk of Parkinson disease (PD) or Alzheimer disease (AD) and discuss their potential mechanisms. Evidence consistently suggests that a higher risk of PD is associated with pesticides and that a higher risk of AD is associated with pesticides, hypertension and high cholesterol levels in middle age, hyperhomocysteinaemia, smoking, traumatic brain injury and depression. There is weak evidence suggesting that higher risk of PD is associated with high milk consumption in men, high iron intake, chronic anaemia and traumatic brain injury. Weak evidence also suggests that a higher risk of AD is associated with high aluminium intake through drinking water, excessive exposure to electromagnetic fields from electrical grids, DM and hyperinsulinaemia, obesity in middle age, excessive alcohol consumption and chronic anaemia. Evidence consistently suggests that a lower risk of PD is associated with hyperuricaemia, tobacco and coffee use, while a lower risk of AD is associated with moderate alcohol consumption, physical exercise, perimenopausal hormone replacement therapy and good cognitive reserve. Weak evidence suggests that lower risk of PD is associated with increased vitamin E intake, alcohol, tea, NSAIDs, and vigorous physical exercise, and that lower risk of AD is associated with the Mediterranean diet, coffee and habitual NSAID consumption. Several environmental factors contribute significantly to risk of PD and AD. Some may already be active in the early stages of life, and some may interact with other genetic factors. Population-based strategies to modify such factors could potentially result in fewer cases of PD or AD. Copyright © 2012 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

  16. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  17. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  18. Infectious Disease Risk Associated with Space Flight

    Science.gov (United States)

    Pierson, Duane L.

    2010-01-01

    This slide presentation opens with views of the shuttle in various stages of preparation for launch, a few moments after launch prior to external fuel tank separation, a few pictures of the earth,and several pictures of astronomical interest. The presentation reviews the factors effecting the risks of infectious disease during space flight, such as the crew, water, food, air, surfaces and payloads and the factors that increase disease risk, the factors affecting the risk of infectious disease during spaceflight, and the environmental factors affecting immunity, such as stress. One factor in space infectious disease is latent viral reactivation, such as herpes. There are comparisons of the incidence of viral reactivation in space, and in other analogous situations (such as bed rest, or isolation). There is discussion of shingles, and the pain and results of treatment. There is a further discussion of the changes in microbial pathogen characteristics, using salmonella as an example of the increased virulence of microbes during spaceflight. A factor involved in the risk of infectious disease is stress.

  19. The strength of the multivariable associations of major risk factors predicting coronary heart disease mortality is homogeneous across different areas of the Seven Countries Study during 50-year follow-up

    NARCIS (Netherlands)

    Menotti, Alessandro; Puddu, Paolo Emilio; Adachi, Hisashi; Kafatos, Anthony; Tolonen, Hanna; Kromhout, Daan

    2017-01-01

    Objectives: To compare the magnitude of multivariable coefficients and hazard ratios of four cardiovascular risk factors across five worldwide regions of the Seven Countries Study in predicting 50-year coronary deaths. Material and methods: A total of 13 cohorts of middle-aged men at entry (40–59

  20. Improving risk stratification for cardiovascular disease

    NARCIS (Netherlands)

    van Wijk, Diederik F.; Boekholdt, S. Matthijs

    2010-01-01

    Evaluation of: Heslop CL, Frohlich JJ, Hill JS. Myeloperoxidase and C-reactive protein have combined utility for long-term prediction of cardiovascular mortality after coronary angiography. J. Am. Coll. Cardiol. 55(11), 1102-1109 (2010). Identifying people at high risk of cardiovascular events is

  1. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS TAKING INTO ACCOUNT QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available There was investigated the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. There were ditermined 70 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme is the definition of risk clinical course of diffuse peritonitis can quantify the severity of the original patients and in most cases is correctly to predict the results of treatment of disease.

  2. Natriuretic peptides and integrated risk assessment for cardiovascular disease

    DEFF Research Database (Denmark)

    Willeit, Peter; Kaptoge, S; Welsh, P.

    2016-01-01

    samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes......BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present...... by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored...

  3. Disease state fingerprint for fall risk assessment.

    Science.gov (United States)

    Similä, Heidi; Immonen, Milla

    2014-01-01

    Fall prevention is an important and complex multifactorial challenge, since one third of people over 65 years old fall at least once every year. A novel application of Disease State Fingerprint (DSF) algorithm is presented for holistic visualization of fall risk factors and identifying persons with falls history or decreased level of physical functioning based on fall risk assessment data. The algorithm is tested with data from 42 older adults, that went through a comprehensive fall risk assessment. Within the study population the Activities-specific Balance Confidence (ABC) scale score, Berg Balance Scale (BBS) score and the number of drugs in use were the three most relevant variables, that differed between the fallers and non-fallers. This study showed that the DSF visualization is beneficial in inspection of an individual's significant fall risk factors, since people have problems in different areas and one single assessment scale is not enough to expose all the people at risk.

  4. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  5. Risk stratification in upper gastrointestinal bleeding; prediction, prevention and prognosis

    NARCIS (Netherlands)

    de Groot, N.L.

    2013-01-01

    In the first part of this thesis we developed a novel prediction score for predicting upper gastrointestinal (GI) bleeding in both NSAID and low-dose aspirin users. Both for NSAIDs and low-dose aspirin use risk scores were developed by identifying the five most dominant predictors. The risk of upper

  6. Notification of occupational disease and the risk of work disability

    DEFF Research Database (Denmark)

    Kolstad, Henrik A; Christensen, Michael V; Jensen, Lone Donbæk

    2012-01-01

    for patients who were not working. CONCLUSIONS: Notification of an occupational disease may, as an unintended side effect, increase the risk of work disability. A cautious interpretation is warranted because data analyses may not fully have accounted for the poorer vocational prognosis already present......OBJECTIVES: The aim of this study was to analyze if notification of an occupational disease increases the risk of work disability. METHODS: We included 2304 patients examined at the Department of Occupational Medicine, Aarhus University Hospital, 1998-2005 and followed them for two years. A total......, occupational, and social characteristics that predict poorer vocational prognosis. Analyses that adjusted for these differences showed an increased risk of work disability following notification for patients who were working when notified at baseline (HR (adj)1.46, 95% CI 1.17-1.82). No effect was seen...

  7. Diabetes propels the risk for cardiovascular disease

    NARCIS (Netherlands)

    Diepen, van Janna A.; Thiem, Kathrin; Stienstra, Rinke; Riksen, Niels P.; Tack, Cees J.; Netea, Mihai G.

    2016-01-01

    Diabetes strongly predisposes to cardiovascular disease (CVD), the leading cause of mortality in these patients, as well as in the entire population. Hyperglycemia is an important cardiovascular risk factor as shown by the observation that even transient periods of hyperglycemia, despite return

  8. Preeclampsia: at risk for remote cardiovascular disease

    NARCIS (Netherlands)

    Harskamp, Ralf E.; Zeeman, Gerda G.

    2007-01-01

    Epidemiological data indicate that women with preeclampsia are more likely to develop cardiovascular disease (CVD) later in life. Population-based studies relate preeclampsia to an increased risk of later chronic hypertension (RR, 2.00 to 8.00) and cardiovascular morbidity/mortality (RR, 1.3 to

  9. Preeclampsia : At risk for remote cardiovascular disease

    NARCIS (Netherlands)

    Harskamp, Ralf E.; Zeeman, Gerda G.

    2007-01-01

    Epidemiological data indicate that women with preeclampsia are more likely to develop cardiovascular disease (CVD) later in life. Population-based studies relate preeclampsia to an increased risk of later chronic hypertension (RR, 2.00 to 8.00) and cardiovascular morbidity/mortality (RR, 1.3 to

  10. Antioxidant vitamins and coronary heart disease risk

    DEFF Research Database (Denmark)

    Knekt, Paul; Ritz, John; Pereira, Mark A

    2004-01-01

    BACKGROUND: Epidemiologic studies have suggested a lower risk of coronary heart disease (CHD) at higher intakes of fruit, vegetables, and whole grain. Whether this association is due to antioxidant vitamins or some other factors remains unclear. OBJECTIVE: We studied the relation between the intake...

  11. Lifestyle factors and risk of cardiovascular diseases

    NARCIS (Netherlands)

    Hoevenaar-Blom, M.P.

    2013-01-01

    Background

    Evidence is accumulating that lifestyle factors influence the incidence of fatal and non-fatal cardiovascular diseases (CVD). A healthy diet, being physically active, moderate alcohol consumption and not smoking are associated with a lower CVD risk. In

  12. Cardiovascular disease risk factors: a childhood perspective.

    Science.gov (United States)

    Praveen, Pradeep A; Roy, Ambuj; Prabhakaran, Dorairaj

    2013-03-01

    Atherosclerotic cardiovascular disease (CVD) is one of the leading causes of death and disability worldwide including in developing countries like India. Indians are known to be predisposed to CVD, which occur almost a decade earlier in them. Though these diseases manifest in the middle age and beyond, it is now clear that the roots of CVD lie in childhood and adolescence. Many of the conventional risk factors of CVD such as high blood pressure, dyslipidemia, tobacco use, unhealthy diet and obesity have their beginnings in childhood and then track overtime. It is thus important to screen and identify these risk factors early and treat them to prevent onset of CVD. Similarly community based strategies to prevent onset of these risk factors is imperative to tackle this burgeoning public health crisis especially in countries like ours with limited resources.

  13. Sacrococcygeal pilonidal disease: analysis of previously proposed risk factors

    Directory of Open Access Journals (Sweden)

    Ali Harlak

    2010-01-01

    Full Text Available PURPOSE: Sacrococcygeal pilonidal disease is a source of one of the most common surgical problems among young adults. While male gender, obesity, occupations requiring sitting, deep natal clefts, excessive body hair, poor body hygiene and excessive sweating are described as the main risk factors for this disease, most of these need to be verified with a clinical trial. The present study aimed to evaluate the value and effect of these factors on pilonidal disease. METHOD: Previously proposed main risk factors were evaluated in a prospective case control study that included 587 patients with pilonidal disease and 2,780 healthy control patients. RESULTS: Stiffness of body hair, number of baths and time spent seated per day were the three most predictive risk factors. Adjusted odds ratios were 9.23, 6.33 and 4.03, respectively (p<0.001. With an adjusted odds ratio of 1.3 (p<.001, body mass index was another risk factor. Family history was not statistically different between the groups and there was no specific occupation associated with the disease. CONCLUSIONS: Hairy people who sit down for more than six hours a day and those who take a bath two or less times per week are at a 219-fold increased risk for sacrococcygeal pilonidal disease than those without these risk factors. For people with a great deal of hair, there is a greater need for them to clean their intergluteal sulcus. People who engage in work that requires sitting in a seat for long periods of time should choose more comfortable seats and should also try to stand whenever possible.

  14. Screening for Peripheral Artery Disease and Cardiovascular Disease Risk Assessment with Ankle Brachial Index in Adults

    Science.gov (United States)

    ... Force Recommendations Screening for Peripheral Artery Disease and Cardiovascular Disease Risk Assessment with Ankle Brachial Index in Adults ... on Screening for Peripheral Artery Disease (PAD) and Cardiovascular Disease (CVD) Risk Assessment with Ankle Brachial Index (ABI) ...

  15. Chronic disease risk factors among hotel workers

    Science.gov (United States)

    Gawde, Nilesh Chandrakant; Kurlikar, Prashika R.

    2016-01-01

    Context: Non-communicable diseases have emerged as a global health issue. Role of occupation in pathogenesis of non-communicable diseases has not been explored much especially in the hospitality industry. Aims: Objectives of this study include finding risk factor prevalence among hotel workers and studying relationship between occupational group and chronic disease risk factors chiefly high body mass index. Settings and Design: A cross-sectional study was conducted among non-managerial employees from classified hotels in India. Materials and Methods: The study participants self-administered pre-designed pilot-tested questionnaires. Statistical analysis used: The risk factor prevalence rates were expressed as percentages. Chi-square test was used for bi-variate analysis. Overweight was chosen as ‘outcome’ variable of interest and binary multi-logistic regression analysis was used to identify determinants. Results: The prevalence rates of tobacco use, alcohol use, inadequate physical activity and inadequate intake of fruits and vegetables were 32%, 49%, 24% and 92% respectively among hotel employees. Tobacco use was significantly common among those in food preparation and service, alcohol use among those in food service and security and leisure time physical activity among front office workers. More than two-fifths (42.7%) were overweight. Among the hotel workers, those employed in food preparation and security had higher odds of 1.650 (CI: 1.025 – 2.655) and 3.245 (CI: 1.296 – 8.129) respectively of being overweight. Conclusions: Prevalence of chronic disease risk factors is high among hotel workers. Risk of overweight is significantly high in food preparation and security departments and workplace interventions are necessary to address these risks PMID:27390474

  16. Chronic disease risk factors among hotel workers.

    Science.gov (United States)

    Gawde, Nilesh Chandrakant; Kurlikar, Prashika R

    2016-01-01

    Non-communicable diseases have emerged as a global health issue. Role of occupation in pathogenesis of non-communicable diseases has not been explored much especially in the hospitality industry. Objectives of this study include finding risk factor prevalence among hotel workers and studying relationship between occupational group and chronic disease risk factors chiefly high body mass index. A cross-sectional study was conducted among non-managerial employees from classified hotels in India. The study participants self-administered pre-designed pilot-tested questionnaires. The risk factor prevalence rates were expressed as percentages. Chi-square test was used for bi-variate analysis. Overweight was chosen as 'outcome' variable of interest and binary multi-logistic regression analysis was used to identify determinants. The prevalence rates of tobacco use, alcohol use, inadequate physical activity and inadequate intake of fruits and vegetables were 32%, 49%, 24% and 92% respectively among hotel employees. Tobacco use was significantly common among those in food preparation and service, alcohol use among those in food service and security and leisure time physical activity among front office workers. More than two-fifths (42.7%) were overweight. Among the hotel workers, those employed in food preparation and security had higher odds of 1.650 (CI: 1.025 - 2.655) and 3.245 (CI: 1.296 - 8.129) respectively of being overweight. Prevalence of chronic disease risk factors is high among hotel workers. Risk of overweight is significantly high in food preparation and security departments and workplace interventions are necessary to address these risks.

  17. Predictability of cardiovascular risks by psychological measures

    Czech Academy of Sciences Publication Activity Database

    Šolcová, Iva; Kebza, V.

    2008-01-01

    Roč. 23, č. 1 (2008), s. 241-241 ISSN 0887-0446 R&D Projects: GA ČR GA406/06/0747 Institutional research plan: CEZ:AV0Z70250504 Keywords : CVD risks * psychological measures * physiological risks Subject RIV: AN - Psychology

  18. Predicting risk of cancer during HIV infection

    DEFF Research Database (Denmark)

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah

    2013-01-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection.......To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection....

  19. Global Variance Risk Premium and Forex Return Predictability

    OpenAIRE

    Aloosh, Arash

    2014-01-01

    In a long-run risk model with stochastic volatility and frictionless markets, I express expected forex returns as a function of consumption growth variances and stock variance risk premiums (VRPs)—the difference between the risk-neutral and statistical expectations of market return variation. This provides a motivation for using the forward-looking information available in stock market volatility indices to predict forex returns. Empirically, I find that stock VRPs predict forex returns at a ...

  20. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  1. Perceived Vulnerability to Disease Predicts Environmental Attitudes

    Science.gov (United States)

    Prokop, Pavol; Kubiatko, Milan

    2014-01-01

    Investigating predictors of environmental attitudes may bring valuable benefits in terms of improving public awareness about biodiversity degradation and increased pro-environmental behaviour. Here we used an evolutionary approach to study environmental attitudes based on disease-threat model. We hypothesized that people vulnerable to diseases may…

  2. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  3. Vital Exhaustion and Coronary Heart Disease Risk

    DEFF Research Database (Denmark)

    Frestad, Daria; Prescott, Eva

    2017-01-01

    INFO (1980 to July 2015; articles in English and published articles only), and bibliographies. Information on aim, study design, sample size, inclusion and exclusion criteria, assessment methods of psychological risk factors, and results of crude and adjusted regression analyses were abstracted independently......OBJECTIVES: The construct of vital exhaustion has been identified as a potential independent psychological risk factor for incident and recurrent coronary heart disease (CHD). Despite several decades of research, no systematic review or meta-analysis has previously attempted to collate.......22-1.85) for prospective studies, and 2.61 (95% CI = 1.66-4.10) for case-control studies using hospital controls. Risk of recurrent events in patients with CHD was 2.03 (95% CI = 1.54-2.68). The pooled adjusted risk of chronic heart failure in healthy populations was 1.37 (95% CI = 1.21-1.56), but this was based...

  4. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    2010-02-01

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  5. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community

    OpenAIRE

    Fisher, Stacey; Hsu, Amy; Mojaverian, Nassim; Taljaard, Monica; Huyer, Gregory; Manuel, Douglas G; Tanuseputro, Peter

    2017-01-01

    Introduction The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modellin...

  6. Inflammatory bowel disease increases the risk of Parkinson's disease

    DEFF Research Database (Denmark)

    Villumsen, Marie; Aznar, Susana; Pakkenberg, Bente

    2018-01-01

    OBJECTIVE: Intestinal inflammation has been suggested to play a role in development of Parkinson's disease (PD) and multiple system atrophy (MSA). To test the hypothesis that IBD is associated with risk of PD and MSA, we performed a nationwide population-based cohort study. DESIGN: The cohort...... patients with UC (HR=1.35; 95% CI 1.20 to 1.52) and not significantly different among patients with Crohn's disease (HR=1.12; 95% CI 0.89 to 1.40). CONCLUSIONS: This nationwide, unselected, cohort study shows a significant association between IBD and later occurrence of PD, which is consistent with recent...

  7. Predictive risk factors for persistent postherniotomy pain

    DEFF Research Database (Denmark)

    Aasvang, Eske K; Gmaehle, Eliza; Hansen, Jeanette B

    2010-01-01

    BACKGROUND: Persistent postherniotomy pain (PPP) affects everyday activities in 5-10% of patients. Identification of predisposing factors may help to identify the risk groups and guide anesthetic or surgical procedures in reducing risk for PPP. METHODS: A prospective study was conducted in 464...... patients undergoing open or laparoscopic transabdominal preperitoneal elective groin hernia repair. Primary outcome was identification of risk factors for substantial pain-related functional impairment at 6 months postoperatively assessed by the validated Activity Assessment Scale (AAS). Data on potential...... risk factors for PPP were collected preoperatively (pain from the groin hernia, preoperative AAS score, pain from other body regions, and psychometric assessment). Pain scores were collected on days 7 and 30 postoperatively. Sensory functions including pain response to tonic heat stimulation were...

  8. Develop mental dyslexia: predicting individual risk

    OpenAIRE

    Thompson, PA; Hulme, C; Nash, HM; Gooch, Deborah; Hayiou-Thomas, E; Snowling, MJ

    2015-01-01

    Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited...

  9. Calibration plots for risk prediction models in the presence of competing risks

    DEFF Research Database (Denmark)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-01-01

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks...... prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves...

  10. Risk of cardiovascular disease following radiation exposure

    International Nuclear Information System (INIS)

    Trivedi, A.; Vlahovich, S.; Cornett, R.J.

    2001-01-01

    Excess radiation-induced cardiac mortalities have been reported among radiotherapy patients. Many case reports describe the occurrence of atherosclerosis following radiotherapy for Hodgkin's disease and breast cancer. Some case reports describe the cerebral infarction following radiotherapy to neck region, and of peripheral vascular disease of the lower extremities following radiotherapy to the pelvic region. The association of atomic bomb radiation and cardiovascular disease has been examined recently by incidence studies and prevalence studies of various endpoints of atherosclerosis; all endpoints indicated an increase of cardiovascular disease in the exposed group. It is almost certain that the cardiovascular disease is higher among atomic bomb survivors. However, since a heavy exposure of 10-40 Gy is delivered in radiotherapy and the bomb survivors were exposed to radiation at high dose and dose-rate, the question is whether the results can be extrapolated to individuals exposed to lower levels of radiation. Some recent epidemiological studies on occupationally exposed workers and population living near Chernobyl have provided the evidence for cardiovascular disease being a significant late effect at relatively low doses of radiation. However, the issue of non-cancer mortality from radiation is complicated by lack of adequate information on doses, and many other confounding factors (e.g., smoking habits or socio-economic status). This presentation will evaluate possible radiobiological mechanisms for radiation-induced cardiovascular disease, and will address its relevance to radiation protection management at low doses and what the impact might be on future radiation risk assessments. (authors)

  11. Emerging infectious disease outbreaks: estimating disease risk in Australian blood donors travelling overseas.

    Science.gov (United States)

    Coghlan, A; Hoad, V C; Seed, C R; Flower, R Lp; Harley, R J; Herbert, D; Faddy, H M

    2018-01-01

    International travel assists spread of infectious pathogens. Australians regularly travel to South-eastern Asia and the isles of the South Pacific, where they may become infected with infectious agents, such as dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) viruses that pose a potential risk to transfusion safety. In Australia, donors are temporarily restricted from donating for fresh component manufacture following travel to many countries, including those in this study. We aimed to estimate the unmitigated transfusion-transmission (TT) risk from donors travelling internationally to areas affected by emerging infectious diseases. We used the European Up-Front Risk Assessment Tool, with travel and notification data, to estimate the TT risk from donors travelling to areas affected by disease outbreaks: Fiji (DENV), Bali (DENV), Phuket (DENV), Indonesia (CHIKV) and French Polynesia (ZIKV). We predict minimal risk from travel, with the annual unmitigated risk of an infected component being released varying from 1 in 1·43 million to disease outbreak areas to source plasma collection provides a simple and effective risk management approach. © 2017 International Society of Blood Transfusion.

  12. Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

    NARCIS (Netherlands)

    Hartemink, N.; Vanwambeke, S.O.; Purse, B.V.; Gilbert, M.; Van Dyck, H.

    2015-01-01

    Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional

  13. Water erosion risk prediction in eucalyptus plantations

    Directory of Open Access Journals (Sweden)

    Mayesse Aparecida da Silva

    2014-04-01

    Full Text Available Eucalyptus plantations are normally found in vulnerable ecosystems such as steep slope, soil with low natural fertility and lands that were degraded by agriculture. The objective of this study was to obtain Universal Soil Loss Equation (USLE factors and use them to estimate water erosion risk in regions with eucalyptus planted. The USLE factors were obtained in field plots under natural rainfall in the Rio Doce Basin, MG, Brazil, and the model applied to assess erosion risk using USLE in a Geographic Information System. The study area showed rainfall-runoff erosivity values from 10,721 to 10,642 MJ mm ha-1 h-1 yr-1. Some soils (Latosols had very low erodibility values (2.0 x 10-4 and 1.0 x 10-4t h MJ-1 mm-1, the topographic factor ranged from 0.03 to 10.57 and crop and management factor values obtained for native forest, eucalyptus and planted pasture were 0.09, 0.12 and 0.22, respectively. Water erosion risk estimates for current land use indicated that the areas where should receive more attention were mainly areas with greater topographic factors and those with Cambisols. Planning of forestry activities in this region should consider implementation of other conservation practices beyond those already used, reducing areas with a greater risk of soil erosion and increasing areas with very low risk.

  14. Predicting risk and the emergence of schizophrenia.

    LENUS (Irish Health Repository)

    Clarke, Mary C

    2012-09-01

    This article gives an overview of genetic and environmental risk factors for schizophrenia. The presence of certain molecular, biological, and psychosocial factors at certain points in the life span, has been linked to later development of schizophrenia. All need to be considered in the context of schizophrenia as a lifelong brain disorder. Research interest in schizophrenia is shifting to late childhood\\/early adolescence for screening and preventative measures. This article discusses those environmental risk factors for schizophrenia for which there is the largest evidence base.

  15. Predicting Acute Exacerbations in Chronic Obstructive Pulmonary Disease.

    Science.gov (United States)

    Samp, Jennifer C; Joo, Min J; Schumock, Glen T; Calip, Gregory S; Pickard, A Simon; Lee, Todd A

    2018-03-01

    With increasing health care costs that have outpaced those of other industries, payers of health care are moving from a fee-for-service payment model to one in which reimbursement is tied to outcomes. Chronic obstructive pulmonary disease (COPD) is a disease where this payment model has been implemented by some payers, and COPD exacerbations are a quality metric that is used. Under an outcomes-based payment model, it is important for health systems to be able to identify patients at risk for poor outcomes so that they can target interventions to improve outcomes. To develop and evaluate predictive models that could be used to identify patients at high risk for COPD exacerbations. This study was retrospective and observational and included COPD patients treated with a bronchodilator-based combination therapy. We used health insurance claims data to obtain demographics, enrollment information, comorbidities, medication use, and health care resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient (primary discharge diagnosis for COPD), outpatient, and emergency department (outpatient/emergency department visits with a COPD diagnosis plus an acute prescription for an antibiotic or corticosteroid within 5 days) exacerbations. The cohort was split into training (75%) and validation (25%) sets. Within the training cohort, stepwise logistic regression models were created to evaluate risk of exacerbations based on factors measured during the baseline period. Models were evaluated using sensitivity, specificity, and positive and negative predictive values. The base model included all confounding or effect modifier covariates. Several other models were explored using different sets of observations and variables to determine the best predictive model. There were 478,772 patients included in the analytic sample, of which 40.5% had exacerbations during the outcome period. Patients with

  16. Risk assessment methodologies for predicting phosphorus losses

    NARCIS (Netherlands)

    Schoumans, O.F.; Chardon, W.J.

    2003-01-01

    Risk assessment parameters are needed to assess the contribution of phosphorus (P) losses from soil to surface water, and the effectiveness of nutrient and land management strategies for the reduction of P loss. These parameters need to take into account the large temporal and spatial variation in P

  17. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  18. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  19. Alcoholic Cirrhosis Increases Risk for Autoimmune Diseases

    DEFF Research Database (Denmark)

    Grønbæk, Lisbet; Vilstrup, Hendrik; Deleuran, Bent

    2015-01-01

    IRR, 1.56; 95% CI, 1.26-1.92), celiac disease (aIRR, 5.12; 95% CI, 2.58-10.16), pernicious anemia (aIRR, 2.35; 95% CI, 1.50-3.68), and psoriasis (aIRR, 4.06; 95% CI, 3.32-4.97). There was no increase in the incidence rate for rheumatoid arthritis (aIRR, 0.89; 95% CI, 0.69-1.15); the incidence rate......BACKGROUND & AIMS: Alcoholic cirrhosis is associated with hyperactivation and dysregulation of the immune system. In addition to its ability to increase risk for infections, it also may increase the risk for autoimmune diseases. We studied the incidence of autoimmune diseases among patients...... (controls) of the same sex and age. The incidence rates of various autoimmune diseases were compared between patients with cirrhosis and controls and adjusted for the number of hospitalizations in the previous year (a marker for the frequency of clinical examination). RESULTS: Of the 24,679 patients...

  20. Elderly fall risk prediction using static posturography

    OpenAIRE

    Howcroft, Jennifer; Lemaire, Edward D.; Kofman, Jonathan; McIlroy, William E.

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed w...

  1. Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?

    DEFF Research Database (Denmark)

    Hansen, Viktor J.; Gromov, Kirill; Lebrun, Lauren M

    2015-01-01

    BACKGROUND: Payers of health services and policymakers place a major focus on cost containment in health care. Studies have shown that early planning of discharge is essential in reducing length of stay and achieving financial benefit; tools that can help predict discharge disposition would...... populations is unknown. A low RAPT score is reported to indicate a high risk of needing any form of inpatient rehabilitation after TJA, including short-term nursing facilities. QUESTIONS/PURPOSES: This study attempts (1) to assess predictive accuracy of the RAPT on US patients undergoing total hip and knee....... Based on our findings, the risk categories in our populations should be high risk intermediate risk 7 to 10, and low risk > 10. CONCLUSIONS: The RAPT accurately predicted discharge disposition for high- and low-risk patients in our cohort. Based on our data, intermediate-risk patients should...

  2. Standard cardiovascular disease risk algorithms underestimate the risk of cardiovascular disease in schizophrenia: evidence from a national primary care database.

    Science.gov (United States)

    McLean, Gary; Martin, Julie Langan; Martin, Daniel J; Guthrie, Bruce; Mercer, Stewart W; Smith, Daniel J

    2014-10-01

    Schizophrenia is associated with increased cardiovascular mortality. Although cardiovascular disease (CVD) risk prediction algorithms are widely in the general population, their utility for patients with schizophrenia is unknown. A primary care dataset was used to compare CVD risk scores (Joint British Societies (JBS) score), cardiovascular risk factors, rates of pre-existing CVD and age of first diagnosis of CVD for schizophrenia (n=1997) relative to population controls (n=215,165). Pre-existing rates of CVD and the recording of risk factors for those without CVD were higher in the schizophrenia cohort in the younger age groups, for both genders. Those with schizophrenia were more likely to have a first diagnosis of CVD at a younger age, with nearly half of men with schizophrenia plus CVD diagnosed under the age of 55 (schizophrenia men 46.1% vs. control men 34.8%, pschizophrenia women 28.9% vs. control women 23.8%, prisk factors within the schizophrenia group, only a very small percentage (3.2% of men and 7.5% of women) of those with schizophrenia under age 55 were correctly identified as high risk for CVD according to the JBS risk algorithm. The JBS2 risk score identified only a small proportion of individuals with schizophrenia under the age of 55 as being at high risk of CVD, despite high rates of risk factors and high rates of first diagnosis of CVD within this age group. The validity of CVD risk prediction algorithms for schizophrenia needs further research. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease.

    Science.gov (United States)

    Ng, Kok Pin; Pascoal, Tharick A; Mathotaarachchi, Sulantha; Chung, Chang-Oh; Benedet, Andréa L; Shin, Monica; Kang, Min Su; Li, Xiaofeng; Ba, Maowen; Kandiah, Nagaendran; Rosa-Neto, Pedro; Gauthier, Serge

    2017-05-09

    To identify regional brain metabolic dysfunctions associated with neuropsychiatric symptoms (NPS) in preclinical Alzheimer disease (AD). We stratified 115 cognitively normal individuals into preclinical AD (both amyloid and tau pathologies present), asymptomatic at risk for AD (either amyloid or tau pathology present), or healthy controls (no amyloid or tau pathology present) using [ 18 F]florbetapir PET and CSF phosphorylated tau biomarkers. Regression and voxel-based regression models evaluated the relationships between baseline NPS measured by the Neuropsychiatric Inventory (NPI) and baseline and 2-year change in metabolism measured by [ 18 F]fluorodeoxyglucose (FDG) PET. Individuals with preclinical AD with higher NPI scores had higher [ 18 F]FDG uptake in the posterior cingulate cortex (PCC), ventromedial prefrontal cortex, and right anterior insula at baseline. High NPI scores predicted subsequent hypometabolism in the PCC over 2 years only in individuals with preclinical AD. Sleep/nighttime behavior disorders and irritability and lability were the components of the NPI that drove this metabolic dysfunction. The magnitude of NPS in preclinical cases, driven by sleep behavior and irritability domains, is linked to transitory metabolic dysfunctions within limbic networks vulnerable to the AD process and predicts subsequent PCC hypometabolism. These findings support an emerging conceptual framework in which NPS constitute an early clinical manifestation of AD pathophysiology. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

  4. College Students' Perceived Disease Risk versus Actual Prevalence Rates

    Science.gov (United States)

    Smith, Matthew Lee; Dickerson, Justin B.; Sosa, Erica T.; McKyer, E. Lisako J.; Ory, Marcia G.

    2012-01-01

    Objective: To compare college students' perceived disease risk with disease prevalence rates. Methods: Data were analyzed from 625 college students collected with an Internet-based survey. Paired t-tests were used to separately compare participants' perceived 10-year and lifetime disease risk for 4 diseases: heart disease, cancer, diabetes, and…

  5. Chronic disease as risk multiplier for disadvantage.

    Science.gov (United States)

    Stutzin Donoso, Francisca

    2018-03-06

    This paper starts by establishing a prima facie case that disadvantaged groups or individuals are more likely to get a chronic disease and are in a disadvantaged position to adhere to chronic treatment despite access through Universal Health Coverage. However, the main aim of this paper is to explore the normative implications of this claim by examining two different but intertwined argumentative lines that might contribute to a better understanding of the ethical challenges faced by chronic disease health policy. The paper develops the argument that certain disadvantages which may predispose to illness might overlap with disadvantages that may hinder self-management, potentially becoming disadvantageous in handling chronic disease. If so, chronic diseases may be seen as disadvantages in themselves, describing a reproduction of disadvantage among the chronically ill and a vicious circle of disadvantage that could both predict and shed light on the catastrophic health outcomes among disadvantaged groups-or individuals-dealing with chronic disease. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  6. Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

    Directory of Open Access Journals (Sweden)

    Hajar Shafiee

    2016-06-01

    Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

  7. Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

    Science.gov (United States)

    Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

    2016-01-01

    Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

  8. Recent development of risk-prediction models for incident hypertension: An updated systematic review.

    Directory of Open Access Journals (Sweden)

    Dongdong Sun

    Full Text Available Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI, age, smoking, blood pressure (BP level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

  9. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    KAUST Repository

    Hou, Siqing

    2018-05-21

    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

  10. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

    2017-06-01

    Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

  12. Improving Cardiovascular Risk Prediction--Biomarkers and Beyond; Implications for Astronaut Selection and Monitoring During Prolonged Spaceflight

    Data.gov (United States)

    National Aeronautics and Space Administration — Our primary objective is to identify and validate novel strategies to enhance global cardiovascular disease (CVD) risk prediction over two time windows: 1) 10-20...

  13. Prospective study of coffee consumption and risk of Parkinson's disease.

    Science.gov (United States)

    Sääksjärvi, K; Knekt, P; Rissanen, H; Laaksonen, M A; Reunanen, A; Männistö, S

    2008-07-01

    To examine the prediction of coffee consumption on the incidence of Parkinson's disease. The study population comprised 6710 men and women, aged 50-79 years and free from Parkinson's disease at the baseline. At baseline, enquiries were made about coffee consumption in a self-administered questionnaire as the average number of cups per day. During a 22-year follow-up, 101 incident cases of Parkinson's disease occurred. Parkinson's disease cases were identified through a nationwide registry of patients receiving medication reimbursement, which is based on certificates from neurologist. After adjustments for age, sex, marital status, education, community density, alcohol consumption, leisure-time physical activity, smoking, body mass index, hypertension and serum cholesterol, the relative risk for subjects drinking 10 or more cups of coffee per day compared with non-drinkers was 0.26 (95% confidence interval 0.07-0.99, P-value for trend=0.18). The association was stronger among overweight persons and among persons with lower serum cholesterol level (P-value for interaction=0.04 and 0.03, respectively). The results support the hypothesis that coffee consumption reduces the risk of Parkinson's disease, but protective effect of coffee may vary by exposure to other factors.

  14. Infectious disease risk in asbestos abatement workers.

    Science.gov (United States)

    Lange, John H; Mastrangelo, Giuseppe; Cegolon, Luca

    2012-08-16

    The current literature reports increased infectious disease occurrence in various construction occupations, as an important contributor to morbidity and mortality arising from employment.These observations should be expanded to asbestos abatement workers, as the abatement can create an environment favorable for bacterial, viral and fungal infections. Asbestos abatement work employs activities resulting in cuts, blisters and abrasions to the skin, work in a dirty environment and exposure to dust, mists and fumes.Furthermore, this population exhibits a high smoking rate which increases the risk of chronic obstructive pulmonary disease and respiratory infections.In addition, these workers also commonly employ respirators, which can accumulate dirt and debris magnifying exposure to microbes. Use of respirators and related types of personal protective equipment, especially if shared and in the close environment experienced by workers, may enhance communicability of these agents, including viruses. Abatement workers need to be provided with information on hazards and targeted by appropriate health education to reduce the infection risk. Epidemiological studies to investigate this risk in asbestos removers are recommended.

  15. Sortilin and the risk of cardiovascular disease.

    Science.gov (United States)

    Coutinho, Maria Francisca; Bourbon, Mafalda; Prata, Maria João; Alves, Sandra

    2013-10-01

    Plasma low-density lipoprotein cholesterol (LDL-C) levels are a key determinant of the risk of cardiovascular disease, which is why many studies have attempted to elucidate the pathways that regulate its metabolism. Novel latest-generation sequencing techniques have identified a strong association between the 1p13 locus and the risk of cardiovascular disease caused by changes in plasma LDL-C levels. As expected for a complex phenotype, the effects of variation in this locus are only moderate. Even so, knowledge of the association is of major importance, since it has unveiled a new metabolic pathway regulating plasma cholesterol levels. Crucial to this discovery was the work of three independent teams seeking to clarify the biological basis of this association, who succeeded in proving that SORT1, encoding sortilin, was the gene in the 1p13 locus involved in LDL metabolism. SORT1 was the first gene identified as determining plasma LDL levels to be mechanistically evaluated and, although the three teams used different, though appropriate, experimental methods, their results were in some ways contradictory. Here we review all the experiments that led to the identification of the new pathway connecting sortilin with plasma LDL levels and risk of myocardial infarction. The regulatory mechanism underlying this association remains unclear, but its discovery has paved the way for considering previously unsuspected therapeutic targets and approaches. Copyright © 2013 Sociedade Portuguesa de Cardiologia. Published by Elsevier España. All rights reserved.

  16. Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

    DEFF Research Database (Denmark)

    Di Angelantonio, Emanuele; Gao, Pei; Khan, Hassan

    2014-01-01

    IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of c...

  17. Obesity Risk Prediction among Women of Upper Egypt: The impact ...

    African Journals Online (AJOL)

    Obesity Risk Prediction among Women of Upper Egypt: The impact of FTO ... with increased obesity risk but there is a lack of association with diabetes. ... (as certain foods or gene therapy) will prevent the percentage of women who is affected ...

  18. Risk score for predicting long-term mortality after coronary artery bypass graft surgery.

    Science.gov (United States)

    Wu, Chuntao; Camacho, Fabian T; Wechsler, Andrew S; Lahey, Stephen; Culliford, Alfred T; Jordan, Desmond; Gold, Jeffrey P; Higgins, Robert S D; Smith, Craig R; Hannan, Edward L

    2012-05-22

    No simplified bedside risk scores have been created to predict long-term mortality after coronary artery bypass graft surgery. The New York State Cardiac Surgery Reporting System was used to identify 8597 patients who underwent isolated coronary artery bypass graft surgery in July through December 2000. The National Death Index was used to ascertain patients' vital statuses through December 31, 2007. A Cox proportional hazards model was fit to predict death after CABG surgery using preprocedural risk factors. Then, points were assigned to significant predictors of death on the basis of the values of their regression coefficients. For each possible point total, the predicted risks of death at years 1, 3, 5, and 7 were calculated. It was found that the 7-year mortality rate was 24.2 in the study population. Significant predictors of death included age, body mass index, ejection fraction, unstable hemodynamic state or shock, left main coronary artery disease, cerebrovascular disease, peripheral arterial disease, congestive heart failure, malignant ventricular arrhythmia, chronic obstructive pulmonary disease, diabetes mellitus, renal failure, and history of open heart surgery. The points assigned to these risk factors ranged from 1 to 7; possible point totals for each patient ranged from 0 to 28. The observed and predicted risks of death at years 1, 3, 5, and 7 across patient groups stratified by point totals were highly correlated. The simplified risk score accurately predicted the risk of mortality after coronary artery bypass graft surgery and can be used for informed consent and as an aid in determining treatment choice.

  19. Risk avoidance in sympatric large carnivores: reactive or predictive?

    Science.gov (United States)

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

    2013-09-01

    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  20. Network-based prediction and knowledge mining of disease genes.

    Science.gov (United States)

    Carson, Matthew B; Lu, Hui

    2015-01-01

    In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network

  1. Pregnancy and the risk of autoimmune disease.

    LENUS (Irish Health Repository)

    Khashan, Ali S

    2012-01-31

    Autoimmune diseases (AID) predominantly affect women of reproductive age. While basic molecular studies have implicated persisting fetal cells in the mother in some AID, supportive epidemiological evidence is limited. We investigated the effect of vaginal delivery, caesarean section (CS) and induced abortion on the risk of subsequent maternal AID. Using the Danish Civil Registration System (CRS) we identified women who were born between 1960 and 1992. We performed data linkage between the CRS other Danish national registers to identify women who had a pregnancy and those who developed AID. Women were categorised into 4 groups; nulligravida (control group), women who had 1st child by vaginal delivery, whose 1st delivery was by CS and who had abortions. Log-linear Poisson regression with person-years was used for data analysis adjusting for several potential confounders. There were 1,035,639 women aged >14 years and 25,570 developed AID: 43.4% nulligravida, 44.3% had their first pregnancy delivered vaginally, 7.6% CS and 4.1% abortions. The risk of AID was significantly higher in the 1st year after vaginal delivery (RR = 1.1[1.0, 1.2]) and CS (RR = 1.3[1.1, 1.5]) but significantly lower in the 1st year following abortion (RR = 0.7[0.6, 0.9]). These results suggest an association between pregnancy and the risk of subsequent maternal AID. Increased risks of AID after CS may be explained by amplified fetal cell traffic at delivery, while decreased risks after abortion may be due to the transfer of more primitive fetal stem cells. The increased risk of AID in the first year after delivery may also be related to greater testing during pregnancy.

  2. Fall risk factors in Parkinson's disease.

    Science.gov (United States)

    Gray, P; Hildebrand, K

    2000-08-01

    Parkinson's disease (PD) is a neurodegenerative disorder characterized by tremor, rigidity, bradykinesia, gait disturbance, and postural instability. Patients with PD suffer frequent falls, yet little research has been done to identify risks specific to PD patients. The objective of this study was to identify the risk factors associated with falls for PD patients through the collection of demographic, environmental, and medical information as well as fall diaries completed during a 3-month period. Patients with a diagnosis of idiopathic PD, with and without falls, were included in the study provided they could stand and walk and had no other condition that could predispose them to falls. Of the 118 participants, 59% reported one or more falls. A total of 237 falls were reported. Duration and severity of PD symptoms, particularly freezing, involuntary movements, and walking and postural difficulties, were significantly associated with an increased risk of falls. Other factors associated with falls were postural hypotension and daily intake of alcohol. Forty percent of falls resulted in injury, but serious injury was rare. The findings have implications for reducing the risk of falls through patient education.

  3. Head injury and risk for Parkinson disease

    DEFF Research Database (Denmark)

    Kenborg, Line; Rugbjerg, Kathrine; Lee, Pei-Chen

    2015-01-01

    in medical records. Patients were matched to 1,785 controls randomly selected from the Danish Central Population Register on sex and year of birth. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. RESULTS: We observed no association between any head......OBJECTIVE: To examine the association between head injuries throughout life and the risk for Parkinson disease (PD) in an interview-based case-control study. METHODS: We identified 1,705 patients diagnosed with PD at 10 neurologic centers in Denmark in 1996-2009 and verified their diagnoses...

  4. Serum YKL-40 predicts long-term mortality in patients with stable coronary disease

    DEFF Research Database (Denmark)

    Harutyunyan, Marina; Gøtze, Jens P; Winkel, Per

    2013-01-01

    We investigated whether the inflammatory biomarker YKL-40 could improve the long-term prediction of death made by common risk factors plus high-sensitivity C-reactive protein (hs-CRP) and N-terminal-pro-B natriuretic peptide (NT-proBNP) in patients with stable coronary artery disease (CAD)....

  5. Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

    Directory of Open Access Journals (Sweden)

    Nikol Snoeren

    Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

  6. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety

    DEFF Research Database (Denmark)

    Mcneill, Ilona M.; Dunlop, Patrick D.; Skinner, Timothy C.

    2016-01-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single...... control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction...

  7. Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

    Science.gov (United States)

    Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

    2017-12-01

    The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

  8. Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

    Directory of Open Access Journals (Sweden)

    Jitendra Jonnagaddala

    2015-01-01

    Full Text Available Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

  9. Lifestyle Decreases Risk Factors for Cardiovascular Diseases

    Science.gov (United States)

    Slavíček, Jaroslav; Kittnar, Otomar; Fraser, Gary E.; Medová, Eva; Konečná, Jana; Žižka, Robert; Dohnalová, Alena; Novák, Vladimír

    2009-01-01

    Summary The morbidity and mortality of the cardiovascular diseases is high in the developed countries. The lifestyle changes are capable to decrease it by 50%. The aim of the present study was to measure the parameters of some risk factors before and after a one-week NEW START rehabilitative retreat. 1,349 volunteers, 320 men, 1,029 woman, mean age 51±14.5 (SD) years participated in 30 rehabilitative retreats from 1999–2006 in the Czech Republic, using a low-fat, low-energy, lacto-ovo-vegetarian diet and exercise, in a stress-free environment. Body weight, height, BMI, blood pressure, heart rate, serum cholesterol and blood glucose were measured. Body weight decreased in 1,223 measured persons from 71.2±14.38 (SD) to 70.6±14.02 kg (pSeventh-day Adventists than in controls who never observed the diet and avail the lifestyle programs. The parameters were nonsignificantly changed one year after finishing the retreat in the sample of 68 persons showing the positive effect of retreats. Our results showed, that the intake of a low-fat, low-energy diet, over the course of one week in a stress-free environment, had positive impact on the risk factors of cardiovascular diseases. PMID:19256282

  10. Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

    Science.gov (United States)

    Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

    2015-12-01

    We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk

  11. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

    Science.gov (United States)

    El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

    2015-04-01

    This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

  13. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    Science.gov (United States)

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  14. Circulating Total Bilirubin and Risk of Incident Cardiovascular Disease in the General Population

    NARCIS (Netherlands)

    Kunutsor, Setor K.; Bakker, Stephan J. L.; Gansevoort, Ronald T.; Chowdhury, Rajiv; Dullaart, Robin P. F.

    OBJECTIVE: To assess the association of circulating total bilirubin and cardiovascular disease (CVD) risk in a new prospective study and to determine whether adding information on total bilirubin values to established cardiovascular risk factors is associated with improvement in prediction of CVD

  15. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

  16. Urate predicts rate of clinical decline in Parkinson disease

    Science.gov (United States)

    Ascherio, Alberto; LeWitt, Peter A.; Xu, Kui; Eberly, Shirley; Watts, Arthur; Matson, Wayne R.; Marras, Connie; Kieburtz, Karl; Rudolph, Alice; Bogdanov, Mikhail B.; Schwid, Steven R.; Tennis, Marsha; Tanner, Caroline M.; Beal, M. Flint; Lang, Anthony E.; Oakes, David; Fahn, Stanley; Shoulson, Ira; Schwarzschild, Michael A.

    2009-01-01

    Context The risk of Parkinson disease (PD) and its rate of progression may decline with increasing blood urate, a major antioxidant. Objective To determine whether serum and cerebrospinal fluid (CSF) concentrations of urate predict clinical progression in patients with PD. Design, Setting, and Participants 800 subjects with early PD enrolled in the DATATOP trial. Pre-treatment urate was measured in serum for 774 subjects and in CSF for 713. Main Outcome Measures Treatment-, age- and sex-adjusted hazard ratios (HRs) for clinical disability requiring levodopa therapy, the pre-specified primary endpoint. Results The HR of progressing to endpoint decreased with increasing serum urate (HR for 1 standard deviation increase = 0.82; 95% CI = 0.73 to 0.93). In analyses stratified by α-tocopherol treatment (2,000 IU/day), a decrease in the HR for the primary endpoint was seen only among subjects not treated with α-tocopherol (HR = 0.75; 95% CI = 0.62 to 0.89, versus those treated HR = 0.90; 95% CI = 0.75 to 1.08). Results were similar for the rate of change in the United Parkinson Disease Rating Scale (UPDRS). CSF urate was also inversely related to both the primary endpoint (HR for highest versus lowest quintile = 0.65; 95% CI: 0.54 to 0.96) and to the rate of change in UPDRS. As with serum urate, these associations were present only among subjects not treated with α-tocopherol. Conclusion Higher serum and CSF urate at baseline were associated with slower rates of clinical decline. The findings strengthen the link between urate and PD and the rationale for considering CNS urate elevation as a potential strategy to slow PD progression. PMID:19822770

  17. A Risk Prediction Model for In-hospital Mortality in Patients with Suspected Myocarditis.

    Science.gov (United States)

    Xu, Duo; Zhao, Ruo-Chi; Gao, Wen-Hui; Cui, Han-Bin

    2017-04-05

    Myocarditis is an inflammatory disease of the myocardium that may lead to cardiac death in some patients. However, little is known about the predictors of in-hospital mortality in patients with suspected myocarditis. Thus, the aim of this study was to identify the independent risk factors for in-hospital mortality in patients with suspected myocarditis by establishing a risk prediction model. A retrospective study was performed to analyze the clinical medical records of 403 consecutive patients with suspected myocarditis who were admitted to Ningbo First Hospital between January 2003 and December 2013. A total of 238 males (59%) and 165 females (41%) were enrolled in this study. We divided the above patients into two subgroups (survival and nonsurvival), according to their clinical in-hospital outcomes. To maximize the effectiveness of the prediction model, we first identified the potential risk factors for in-hospital mortality among patients with suspected myocarditis, based on data pertaining to previously established risk factors and basic patient characteristics. We subsequently established a regression model for predicting in-hospital mortality using univariate and multivariate logistic regression analyses. Finally, we identified the independent risk factors for in-hospital mortality using our risk prediction model. The following prediction model for in-hospital mortality in patients with suspected myocarditis, including creatinine clearance rate (Ccr), age, ventricular tachycardia (VT), New York Heart Association (NYHA) classification, gender and cardiac troponin T (cTnT), was established in the study: P = ea/(1 + ea) (where e is the exponential function, P is the probability of in-hospital death, and a = -7.34 + 2.99 × [Ccr model demonstrated that a Ccr prediction model for in-hospital mortality in patients with suspected myocarditis. In addition, sufficient life support during the early stage of the disease might improve the prognoses of patients with

  18. Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

    Directory of Open Access Journals (Sweden)

    Sungkyoung Choi

    2016-12-01

    Full Text Available The success of genome-wide association studies (GWASs has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR, least absolute shrinkage and selection operator (LASSO, and Elastic-Net (EN. We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

  19. Inductive matrix completion for predicting gene-disease associations.

    Science.gov (United States)

    Natarajan, Nagarajan; Dhillon, Inderjit S

    2014-06-15

    Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

  20. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    Science.gov (United States)

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-09-01

    National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

  1. Nucleus basalis of Meynert degeneration precedes and predicts cognitive impairment in Parkinson's disease.

    Science.gov (United States)

    Schulz, Jonathan; Pagano, Gennaro; Fernández Bonfante, Juan Alberto; Wilson, Heather; Politis, Marios

    2018-05-01

    Currently, no reliable predictors of cognitive impairment in Parkinson's disease exist. We hypothesized that microstructural changes at grey matter T1-weighted MRI and diffusion tensor imaging in the cholinergic system nuclei and associated limbic pathways underlie cognitive impairment in Parkinson's disease. We performed a cross-sectional comparison between patients with Parkinson's disease with and without cognitive impairment. We also performed a longitudinal 36-month follow-up study of cognitively intact Parkinson's disease patients, comparing patients who remained cognitively intact to those who developed cognitive impairment. Patients with Parkinson's disease with cognitive impairment showed lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert, compared to patients with Parkinson's disease without cognitive impairment. These results were confirmed both with region of interest and voxel-based analyses, and after partial volume correction. Lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert was predictive for developing cognitive impairment in cognitively intact patients with Parkinson's disease, independent of other clinical and non-clinical markers of the disease. Structural and microstructural alterations in entorhinal cortex, amygdala, hippocampus, insula, and thalamus were not predictive for developing cognitive impairment in Parkinson's disease. Our findings provide evidence that degeneration of the nucleus basalis of Meynert precedes and predicts the onset of cognitive impairment, and might be used in a clinical setting as a reliable biomarker to stratify patients at higher risk of cognitive decline.

  2. PERIODONTAL INFECTIONS AS A RISK FACTOR FOR VARIOUS SYSTEMIC DISEASES

    OpenAIRE

    Solanki, Gaurav; Solanki, Renu

    2012-01-01

    A healthy periodontium is needed for the general well being of an individual. However, periodontal diseases are common and periodontal infections are increasingly associated with systemic diseases. The literature is focused on the association between periodontal infections and systemic diseases. The individuals with periodontal disease may be at higher risk for adverse medical outcomes including cardiovascular diseases, respiratory infections, adverse pregnancy outcomes, rheumatoid arthritis ...

  3. The impact of global environmental change on vector-borne disease risk: a modelling study

    Directory of Open Access Journals (Sweden)

    Rachel Lowe, PhD

    2018-05-01

    Full Text Available Background: Vector-borne diseases, such as dengue virus, Zika virus, and malaria, are highly sensitive to environmental changes, including variations in climate and land-surface characteristics. The emergence and spread of vector-borne diseases is also exacerbated by anthropogenic activities, such as deforestation, mining, urbanisation, and human mobility, which alter the natural habitats of vectors and increase vector–host interactions. Innovative epidemiological modelling tools can help to understand how environmental conditions interact with socioeconomic risk factors to predict the risk of disease transmission. In recent years, climate-health modelling has benefited from computational advances in fitting complex mathematical models; increasing availability of environmental, socioeconomic, and disease surveillance datasets; and improved ability to understand and model the climate system. Climate forecasts at seasonal time scales tend to improve in quality during El Niño-Southern Oscillation events in certain regions of the tropics. Thus, climate forecasts provide an opportunity to anticipate potential outbreaks of vector-borne diseases from several months to a year in advance. The aim of this study was to develop a framework to incorporate seasonal climate forecasts in predictive disease models to understand the future risk of vector-borne diseases, with a focus on dengue fever in Latin America. Methods: A Bayesian spatiotemporal model framework that quantifies the extent to which environmental and socioeconomic indicators can explain variations in disease risk was designed to disentangle the effects of climate from other risk factors using multi-source data and random effects, which account for unknown and unmeasured sources of spatial, seasonal, and inter-annual variation. The model was used to provide probabilistic predictions of monthly dengue incidence and the probability of exceeding outbreak thresholds, which were established in

  4. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  5. Predictive risk factors for moderate to severe hyperbilirubinemia

    OpenAIRE

    Gláucia Macedo de Lima; Maria Amélia Sayeg Campos Porto; Arnaldo Prata Barbosa; Antonio José Ledo Alves da Cunha

    2007-01-01

    Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetri...

  6. Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

    Directory of Open Access Journals (Sweden)

    Solarin Adewale RT

    2008-05-01

    Full Text Available Abstract Background The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria. Methods Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state. Results Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST (B = 0.308, p = 0.013. While LST (B = -0.478, p = 0.035, rainfall (B = -0.006, p = 0.0005, ferric luvisols (B = 0.539, p = 0.274, dystric nitosols (B = 0.133, p = 0.769 and pellic vertisols (B = 1.386, p = 0.008 soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs

  7. Plasma proteins predict conversion to dementia from prodromal disease.

    Science.gov (United States)

    Hye, Abdul; Riddoch-Contreras, Joanna; Baird, Alison L; Ashton, Nicholas J; Bazenet, Chantal; Leung, Rufina; Westman, Eric; Simmons, Andrew; Dobson, Richard; Sattlecker, Martina; Lupton, Michelle; Lunnon, Katie; Keohane, Aoife; Ward, Malcolm; Pike, Ian; Zucht, Hans Dieter; Pepin, Danielle; Zheng, Wei; Tunnicliffe, Alan; Richardson, Jill; Gauthier, Serge; Soininen, Hilkka; Kłoszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon

    2014-11-01

    The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia. Three multicenter cohorts of cognitively healthy elderly, mild cognitive impairment (MCI), and AD participants with standardized clinical assessments and structural neuroimaging measures were used. Twenty-six candidate proteins were quantified in 1148 subjects using multiplex (xMAP) assays. Sixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85%, and specificity 88%). We have identified 10 plasma proteins strongly associated with disease severity and disease progression. Such markers may be useful for patient selection for clinical trials and assessment of patients with predisease subjective memory complaints. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Chronic Kidney Disease – Where Next? Predicting Outcomes and Planning Care Pathways

    Directory of Open Access Journals (Sweden)

    Angharad Marks

    2014-07-01

    Full Text Available With the introduction of the National Kidney Foundation Kidney Disease Outcomes Quality Initiative chronic kidney disease (CKD guidelines, CKD has been identified as common, particularly in the elderly. The outcomes for those with CKD can be poor: mortality, initiation of renal replacement therapy, and progressive deterioration in kidney function, with its associated complications. In young people with CKD, the risk of poor outcome is high and the social cost substantial, but the actual number of patients affected is relatively small. In the elderly, the risk of poor outcome is substantially lower, but due to the high prevalence of CKD the actual number of poor outcomes attributable to CKD is higher. Predicting which patients are at greatest risk, and being able to tailor care appropriately, has significant potential benefits. Risk prediction models in CKD are being developed and show promise but thus far have limitations. In this review we describe the pathway for developing and evaluating risk prediction tools, and consider what models we have for CKD prediction and where next.

  9. Risk assessment and remedial policy evaluation using predictive modeling

    International Nuclear Information System (INIS)

    Linkov, L.; Schell, W.R.

    1996-01-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment

  10. Review article. Predicting disease onset in clinically healthy people

    Directory of Open Access Journals (Sweden)

    Zeliger . Harold I.

    2016-06-01

    Full Text Available Virtually all human disease is induced by oxidative stress. Oxidative stress, which is caused by toxic environmental exposure, the presence of disease, lifestyle choices, stress, chronic inflammation or combinations of these, is responsible for most disease. Oxidative stress from all sources is additive and it is the total oxidative stress from all sources that induces the onset of most disease. Oxidative stress leads to lipid peroxidation, which in turn produces Malondialdehyde. Serum malondialdehyde level is an additive parameter resulting from all sources of oxidative stress and, therefore, is a reliable indicator of total oxidative stress which can be used to predict the onset of disease in clinically asymptomatic individuals and to suggest the need for treatment that can prevent much human disease.

  11. Preclinical Alzheimer disease and risk of falls.

    Science.gov (United States)

    Stark, Susan L; Roe, Catherine M; Grant, Elizabeth A; Hollingsworth, Holly; Benzinger, Tammie L; Fagan, Anne M; Buckles, Virginia D; Morris, John C

    2013-07-30

    We determined the rate of falls among cognitively normal, community-dwelling older adults, some of whom had presumptive preclinical Alzheimer disease (AD) as detected by in vivo imaging of fibrillar amyloid plaques using Pittsburgh compound B (PiB) and PET and/or by assays of CSF to identify Aβ₄₂, tau, and phosphorylated tau. We conducted a 12-month prospective cohort study to examine the cumulative incidence of falls. Participants were evaluated clinically and underwent PiB PET imaging and lumbar puncture. Falls were reported monthly using an individualized calendar journal returned by mail. A Cox proportional hazards model was used to test whether time to first fall was associated with each biomarker and the ratio of CSF tau/Aβ₄₂ and CSF phosphorylated tau/Aβ₄₂, after adjustment for common fall risk factors. The sample (n = 125) was predominately female (62.4%) and white (96%) with a mean age of 74.4 years. When controlled for ability to perform activities of daily living, higher levels of PiB retention (hazard ratio = 2.95 [95% confidence interval 1.01-6.45], p = 0.05) and of CSF biomarker ratios (p risk factor for falls in older adults. This study suggests that subtle noncognitive changes that predispose older adults to falls are associated with AD and may precede detectable cognitive changes.

  12. Using Earth Observations to Understand and Predict Infectious Diseases

    Science.gov (United States)

    Soebiyanto, Radina P.; Kiang, Richard

    2015-01-01

    This presentation discusses the processes from data collection and processing to analysis involved in unraveling patterns between disease outbreaks and the surrounding environment and meteorological conditions. We used these patterns to estimate when and where disease outbreaks will occur. As a case study, we will present our work on assessing the relationship between meteorological conditions and influenza in Central America. Our work represents the discovery, prescriptive and predictive aspects of data analytics.

  13. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    Science.gov (United States)

    Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

    2017-01-01

    Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

  14. Vitamin D Deficiency : Universal Risk Factor for Multifactorial Diseases?

    NARCIS (Netherlands)

    de Borst, Martin H.; de Boer, Rudolf A.; Stolk, Ronald P.; Slaets, Joris P. J.; Wolffenbuttel, Bruce H. R.; Navis, Gerjan

    In the Western world, the majority of morbidity and mortality are caused by multifactorial diseases. Some risk factors are related to more than one type of disease. These so-called universal risk factors are highly relevant to the population, as reduction of universal risk factors may reduce the

  15. Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

    Science.gov (United States)

    Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

    2016-01-01

    We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

    Science.gov (United States)

    Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

    2017-12-01

    For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Predicted risks of radiogenic cardiac toxicity in two pediatric patients undergoing photon or proton radiotherapy

    International Nuclear Information System (INIS)

    Zhang, Rui; Howell, Rebecca M; Homann, Kenneth; Giebeler, Annelise; Taddei, Phillip J; Mahajan, Anita; Newhauser, Wayne D

    2013-01-01

    Hodgkin disease (HD) and medulloblastoma (MB) are common malignancies found in children and young adults, and radiotherapy is part of the standard treatment. It was reported that these patients who received radiation therapy have an increased risk of cardiovascular late effects. We compared the predicted risk of developing radiogenic cardiac toxicity after photon versus proton radiotherapies for a pediatric patient with HD and a pediatric patient with MB. In the treatment plans, each patient’s heart was contoured in fine detail, including substructures of the pericardium and myocardium. Risk calculations took into account both therapeutic and stray radiation doses. We calculated the relative risk (RR) of cardiac toxicity using a linear risk model and the normal tissue complication probability (NTCP) values using relative seriality and Lyman models. Uncertainty analyses were also performed. The RR values of cardiac toxicity for the HD patient were 7.27 (proton) and 8.37 (photon), respectively; the RR values for the MB patient were 1.28 (proton) and 8.39 (photon), respectively. The predicted NTCP values for the HD patient were 2.17% (proton) and 2.67% (photon) for the myocardium, and were 2.11% (proton) and 1.92% (photon) for the whole heart. The predicted ratios of NTCP values (proton/photon) for the MB patient were much less than unity. Uncertainty analyses revealed that the predicted ratio of risk between proton and photon therapies was sensitive to uncertainties in the NTCP model parameters and the mean radiation weighting factor for neutrons, but was not sensitive to heart structure contours. The qualitative findings of the study were not sensitive to uncertainties in these factors. We conclude that proton and photon radiotherapies confer similar predicted risks of cardiac toxicity for the HD patient in this study, and that proton therapy reduced the predicted risk for the MB patient in this study

  18. Dynamic Bayesian modeling for risk prediction in credit operations

    DEFF Research Database (Denmark)

    Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andres

    2015-01-01

    Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context...

  19. Prediction of postpartum blood transfusion – risk factors and recurrence

    DEFF Research Database (Denmark)

    Wikkelsø, Anne J; Hjortøe, Sofie; Gerds, Thomas A

    2014-01-01

    OBJECTIVE: The aim was to find clinically useful risk factors for postpartum transfusion and to assess the joint predictive value in a population of women with a first and second delivery. METHODS: All Danish women with a first and second delivery from January 2001 to September 2009 who gave birt...

  20. Predicting the risk of mineral deficiencies in grazing animals

    African Journals Online (AJOL)

    lambs to mineral supplements can be used to predict risks of deficiency will be demonstrated. In both cases .... between body size and appetite, the onset of lactation or the feeding of ... possible importance of this in the aetiology of milk fever.

  1. Mountain Risks: From Prediction to Management and Governance

    Directory of Open Access Journals (Sweden)

    David Petley

    2015-05-01

    Full Text Available Reviewed: Mountain Risks: From Prediction to Management and Governance. Edited by Theo Van Asch, Jordi Corominas, Stefan Greiving, Jean-Philippe Malet, and Sterlacchini Simone. Dordrecht, The Netherlands: Springer, 2014. xi + 413 pp. US$ 129.00, € 90.00, € 104.00. Also available as an e-book. ISBN 978-94-007-6768-3.

  2. The role of risk propensity in predicting self-employment.

    Science.gov (United States)

    Nieß, Christiane; Biemann, Torsten

    2014-09-01

    This study aims to untangle the role of risk propensity as a predictor of self-employment entry and self-employment survival. More specifically, it examines whether the potentially positive effect of risk propensity on the decision to become self-employed turns curvilinear when it comes to the survival of the business. Building on a longitudinal sample of 4,973 individuals from the German Socio-Economic Panel, we used event history analyses to evaluate the influence of risk propensity on self-employment over a 7-year time period. Results indicated that whereas high levels of risk propensity positively predicted the decision to become self-employed, the relationship between risk propensity and self-employment survival followed an inverted U-shaped curve. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  3. Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

    Science.gov (United States)

    Wu, Cai; Li, Liang

    2018-05-15

    This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

  4. Predictive factors of thyroid cancer in patients with Graves' disease.

    Science.gov (United States)

    Ren, Meng; Wu, Mu Chao; Shang, Chang Zhen; Wang, Xiao Yi; Zhang, Jing Lu; Cheng, Hua; Xu, Ming Tong; Yan, Li

    2014-01-01

    The best preoperative examination in Graves' disease with thyroid cancer still remains uncertain. The objectives of the present study were to investigate the prevalence of thyroid cancer in Graves' disease patients, and to identify the predictive factors and ultrasonographic features of thyroid cancer that may aid the preoperative diagnosis in Graves' disease. This retrospective study included 423 patients with Graves' disease who underwent surgical treatment from 2002 to 2012 at our institution. The clinical features and ultrasonographic findings of thyroid nodules were recorded. The diagnosis of thyroid cancer was determined according to the pathological results. Thyroid cancer was discovered in 58 of the 423 (13.7 %) surgically treated Graves' disease patients; 46 of those 58 patients had thyroid nodules, and the other 12 patients were diagnosed with incidentally discovered thyroid carcinomas without thyroid nodules. Among the 58 patients with thyroid cancer, papillary microcarcinomas were discovered in 50 patients, and multifocality and lymph node involvement were detected in the other 8 patients. Multivariate regression analysis showed younger age was the only significant factor predictive of metastatic thyroid cancer. Ultrasonographic findings of calcification and intranodular blood flow in thyroid nodules indicate that they are more likely to harbor thyroid cancers. Because the influencing factor of metastatic thyroid cancers in Graves' disease is young age, every suspicious nodule in Graves' disease patients should be evaluated and treated carefully, especially in younger patients because of the potential for metastasis.

  5. Validation of a risk prediction model for Barrett's esophagus in an Australian population.

    Science.gov (United States)

    Ireland, Colin J; Gordon, Andrea L; Thompson, Sarah K; Watson, David I; Whiteman, David C; Reed, Richard L; Esterman, Adrian

    2018-01-01

    Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett's esophagus (BE). While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78-0.87). The Hosmer-Lemeshow statistic was p =0.14. Minimizing false positives and false negatives, the model achieved a sensitivity of 74% and a specificity of 73%. This study has validated a risk prediction model for BE that has a higher sensitivity than previous models.

  6. Predicting risk of violence through a self-appraisal questionnaire

    Directory of Open Access Journals (Sweden)

    José Manuel Andreu-Rodríguez

    2016-07-01

    Full Text Available The Self-Appraisal Questionnaire (SAQ is a self-report that predicts the risk of violence and recidivism and provides relevant information about treatment needs for incarcerated populations. The objective of the present study was to evaluate the concurrent and predictive validity of this self-report in Spanish offenders. The SAQ was administered to 276 offenders recruited from several prisons in Madrid (Spain. SAQ total scores presented high levels of internal consistency (alpha = .92. Correlations of the instrument with violence risk instruments were statistically significant and showed a moderate magnitude, indicating a reasonable degree of concurrent validity. The ROC analysis carried out on the SAQ total score revealed an AUC of .80, showing acceptable accuracy discriminating between violent and nonviolent recidivist groups. It is concluded that the SAQ total score is a reliable and valid measure to estimate violence and recidivism risk in Spanish offenders.

  7. A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

    International Nuclear Information System (INIS)

    Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

    1991-01-01

    The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

  8. Single nucleotide polymorphism of CC chemokine ligand 5 promoter gene in recipients may predict the risk of chronic graft-versus-host disease and its severity after allogeneic transplantation.

    Science.gov (United States)

    Kim, Dong Hwan; Jung, Hee Du; Lee, Nan Young; Sohn, Sang Kyun

    2007-10-15

    Leukocyte trafficking, regulated by chemokine ligands and their receptors, involves in the pathogenesis of graft-versus-host disease (GVHD) including CC ligand 5 (CCL5) or CC receptor 5 (CCR5). The current study analyzed the association of acute or chronic GVHD (cGVHD) with the CCR5/CCL5 gene single nucleotide polymorphisms (SNPs) of recipients and donors. We evaluated the SNPs of CCL5 promoter gene at position -28 (rs1800825)/-403 (rs2107538) and CCR5 gene at 59029 (rs1799987) in 72 recipients and donors using polymerase chain reaction/RFLP (Restriction Fragment Length Polymorphism) methods. With a median follow up of 924 days for survivors (range 48-2,360 days), the CG genotype of CCL5 gene at position -28 in recipients was significantly associated with a higher incidence of cGVHD (P=0.004), extensive cGVHD (P=0.038 by Seattle's criteria), and severe grade of cGVHD at presentation (P=0.017 by prognostic grading by Apkek et al.) compared to CC genotype. In terms of haplotype analysis, the recipients with AG haplotype of CCL5 gene also showed a higher incidence of cGVHD (P=0.003), extensive cGVHD (P=0.023), and more severe grade of cGVHD (P=0.020). However, there was no association of CCL5/CCR5 SNPs with acute GVHD. The donors' genotype of CCL5/CCR5 was not associated with the risk of cGVHD. The CCL5 promoter gene polymorphism of recipients was associated with the risk of cGVHD and its severity. The current study suggested an involvement of CCL5 in leukocyte trafficking for the development of cGVHD.

  9. Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

    Science.gov (United States)

    Wen, Yalu; Shen, Xiaoxi; Lu, Qing

    2018-05-31

    With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

  10. A biological approach to the interspecies prediction of radiation-induced mortality risk

    International Nuclear Information System (INIS)

    Carnes, B.A.; Grahn, D.; Olshansky, S.J.

    1997-01-01

    Evolutionary explanations for why sexually reproducing organisms grow old suggest that the forces of natural selection affect the ages when diseases occur that are subject to a genetic influence (referred to here as intrinsic diseases). When extended to the population level for a species, this logic leads to the general prediction that age-specific death rates from intrinsic causes should begin to rise as the force of selection wanes once the characteristic age of sexual maturity is attained. Results consistent with these predictions have been found for laboratory mice, beagles, and humans where, after adjusting for differences in life span, it was demonstrated that these species share a common age pattern of mortality for intrinsic causes of death. In quantitative models used to predict radiation-induced mortality, risks are often expressed as multiples of those observed in a control population. A control population, however, is an aging population. As such, mortality risks related to exposure must be interpreted relative to the age-specific risk of death associated with aging. Given the previous success in making interspecies predictions of age-related mortality, the purpose of this study was to determine whether radiation-induced mortality observed in one species could also be predicted quantitatively from a model used to describe the mortality consequences of exposure to radiation in a different species. Mortality data for B6CF 1 mice and beagles exposed to 60 Co γ-rays for the duration of life were used for analysis

  11. Dispositional optimism and perceived risk interact to predict intentions to learn genome sequencing results.

    Science.gov (United States)

    Taber, Jennifer M; Klein, William M P; Ferrer, Rebecca A; Lewis, Katie L; Biesecker, Leslie G; Biesecker, Barbara B

    2015-07-01

    Dispositional optimism and risk perceptions are each associated with health-related behaviors and decisions and other outcomes, but little research has examined how these constructs interact, particularly in consequential health contexts. The predictive validity of risk perceptions for health-related information seeking and intentions may be improved by examining dispositional optimism as a moderator, and by testing alternate types of risk perceptions, such as comparative and experiential risk. Participants (n = 496) had their genomes sequenced as part of a National Institutes of Health pilot cohort study (ClinSeq®). Participants completed a cross-sectional baseline survey of various types of risk perceptions and intentions to learn genome sequencing results for differing disease risks (e.g., medically actionable, nonmedically actionable, carrier status) and to use this information to change their lifestyle/health behaviors. Risk perceptions (absolute, comparative, and experiential) were largely unassociated with intentions to learn sequencing results. Dispositional optimism and comparative risk perceptions interacted, however, such that individuals higher in optimism reported greater intentions to learn all 3 types of sequencing results when comparative risk was perceived to be higher than when it was perceived to be lower. This interaction was inconsistent for experiential risk and absent for absolute risk. Independent of perceived risk, participants high in dispositional optimism reported greater interest in learning risks for nonmedically actionable disease and carrier status, and greater intentions to use genome information to change their lifestyle/health behaviors. The relationship between risk perceptions and intentions may depend on how risk perceptions are assessed and on degree of optimism. (c) 2015 APA, all rights reserved.

  12. Calibration plots for risk prediction models in the presence of competing risks.

    Science.gov (United States)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Patients with psoriasis have an increased risk of cardiovascular diseases

    DEFF Research Database (Denmark)

    Ahlehoff, Ole; Gislason, Gunnar; Lindhardsen, Jesper

    2012-01-01

    Psoriasis is a chronic immunoinflammatory disease that affects 2-3% of the population and shares pathophysiologic mechanisms and risk factors with cardiovascular diseases. Studies have suggested psoriasis as an independent risk factor for cardiovascular disease and Danish guidelines...... on cardiovascular risk factor modification in patients with psoriasis and psoriatic arthritis have recently been published. We provide a short review of the current evidence and the Danish guidelines....

  14. Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

    Science.gov (United States)

    Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

    2013-07-01

    Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

  15. The Reliability and Predictive Validity of the Stalking Risk Profile.

    Science.gov (United States)

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  16. Reliability of blood pressure measurement and cardiovascular risk prediction

    NARCIS (Netherlands)

    van der Hoeven, N.V.

    2016-01-01

    High blood pressure is one of the leading risk factors for cardiovascular disease, but difficult to reliably assess because there are many factors which can influence blood pressure including stress, exercise or illness. The first part of this thesis focuses on possible ways to improve the

  17. 'Awareness and attitudes towards total cardiovascular disease risk ...

    African Journals Online (AJOL)

    Microsoft account

    Corresponding author: Dr S Ofori, Department of Internal Medicine, ... regarding total CVD risk assessment in clinical practice among physicians in Port ..... cardiovascular risk for prevention and control of cardiovascular disease in low and.

  18. Haptoglobin phenotypes as a risk factor for coronary artery disease ...

    African Journals Online (AJOL)

    Gehan Hamdy

    2014-04-22

    Apr 22, 2014 ... Recognition of diabetic individuals at greatest risk of developing coronary ..... Early detection of the disease and timely interventions can reduce the morbidity ..... additional risk factor of retinopathy in type 2 diabetes mellitus.

  19. Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.

    Directory of Open Access Journals (Sweden)

    Linda Valeri

    Full Text Available The recent Ebola virus disease (EVD outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered.To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2 in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models.The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic.By combining two common methods-estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models-we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.

  20. Performance of genetic risk factors in prediction of trichloroethylene induced hypersensitivity syndrome.

    Science.gov (United States)

    Dai, Yufei; Chen, Ying; Huang, Hanlin; Zhou, Wei; Niu, Yong; Zhang, Mingrong; Bin, Ping; Dong, Haiyan; Jia, Qiang; Huang, Jianxun; Yi, Juan; Liao, Qijun; Li, Haishan; Teng, Yanxia; Zang, Dan; Zhai, Qingfeng; Duan, Huawei; Shen, Juan; He, Jiaxi; Meng, Tao; Sha, Yan; Shen, Meili; Ye, Meng; Jia, Xiaowei; Xiang, Yingping; Huang, Huiping; Wu, Qifeng; Shi, Mingming; Huang, Xianqing; Yang, Huanming; Luo, Longhai; Li, Sai; Li, Lin; Zhao, Jinyang; Li, Laiyu; Wang, Jun; Zheng, Yuxin

    2015-07-20

    Trichloroethylene induced hypersensitivity syndrome is dose-independent and potentially life threatening disease, which has become one of the serious occupational health issues and requires intensive treatment. To discover the genetic risk factors and evaluate the performance of risk prediction model for the disease, we conducted genomewide association study and replication study with total of 174 cases and 1761 trichloroethylene-tolerant controls. Fifty seven SNPs that exceeded the threshold for genome-wide significance (P < 5 × 10(-8)) were screened to relate with the disease, among which two independent SNPs were identified, that is rs2857281 at MICA (odds ratio, 11.92; P meta = 1.33 × 10(-37)) and rs2523557 between HLA-B and MICA (odds ratio, 7.33; P meta = 8.79 × 10(-35)). The genetic risk score with these two SNPs explains at least 20.9% of the disease variance and up to 32.5-fold variation in inter-individual risk. Combining of two SNPs as predictors for the disease would have accuracy of 80.73%, the area under receiver operator characteristic curves (AUC) scores was 0.82 with sensitivity of 74% and specificity of 85%, which was considered to have excellent discrimination for the disease, and could be considered for translational application for screening employees before exposure.

  1. BRAIN NATRIURETIC PEPTIDE (BNP: BIOMARKER FOR RISK STRATIFICATION AND FUNCTIONAL RECOVERY PREDICTION IN ISCHEMIC STROKE

    Directory of Open Access Journals (Sweden)

    STANESCU Ioana

    2015-02-01

    Full Text Available Functional outcome after cardiovascular and cerebrovascular events is traditionally predicted using demographic and clinical variables like age, gender, blood pressure, cholesterol levels, diabetes status, smoking habits or pre-existing morbidity. Identification of new variables will improve the risk stratification of specific categories of patients. Numerous blood-based biomarkers associated with increased cardiovascular risk have been identified; some of them even predict cardiovascular events. Investigators have tried to produce prediction models by incorporating traditional risk factors and biomarkers. (1. Widely-available, rapidly processed and less expensive biomarkers could be used in the future to guide management of complex cerebrovascular patients in order to maximize their recovery (2 Recently, studies have demonstrated that biomarkers can predict not only the risk for a specific clinical event, but also the risk of death of vascular cause and the functional outcome after cardiovascular or cerebrovascular events. Early prediction of fatal outcome after stroke may improve therapeutic strategies (such as the use of more aggressive treatments or inclusion of patients in clinical trials and guide decision-making processes in order to maximize patient’s chances for survival and recovery. (3 Long term functional outcome after stroke is one of the most difficult variables to predict. Elevated serum levels of brain natriuretic peptide (BNP are powerful predictor of outcomes in patients with cardiovascular disease (heart failure, atrial fibrillation. Potential role of BNP in predicting atrial fibrillation occurrence, cardio-embolic stroke and post-stroke mortality have been proved in many studies. However, data concerning the potential role of BNP in predicting short term and long term functional outcomes after stroke remain controversial.

  2. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients: CoCoNet study.

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-06-01

    There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings.

  3. Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients

    Science.gov (United States)

    Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

    2016-01-01

    Abstract There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings. PMID:27310982

  4. Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

    Science.gov (United States)

    Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

    2015-05-01

    Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  5. Dietary fiber and risk of coronary heart disease

    DEFF Research Database (Denmark)

    Pereira, Mark A; O'Reilly, Eilis; Augustsson, Katarina

    2004-01-01

    BACKGROUND: Few epidemiologic studies of dietary fiber intake and risk of coronary heart disease have compared fiber types (cereal, fruit, and vegetable) or included sex-specific results. The purpose of this study was to conduct a pooled analysis of dietary fiber and its subtypes and risk...... of coronary heart disease. METHODS: We analyzed the original data from 10 prospective cohort studies from the United States and Europe to estimate the association between dietary fiber intake and the risk of coronary heart disease. RESULTS: Over 6 to 10 years of follow-up, 5249 incident total coronary cases...... associated with risk of coronary heart disease....

  6. Chronic wasting disease risk analysis workshop: An integrative approach

    Science.gov (United States)

    Gillette, Shana; Dein, Joshua; Salman, Mo; Richards, Bryan; Duarte, Paulo

    2004-01-01

    Risk analysis tools have been successfully used to determine the potential hazard associated with disease introductions and have facilitated management decisions designed to limit the potential for disease introduction. Chronic Wasting Disease (CWD) poses significant challenges for resource managers due to an incomplete understanding of disease etiology and epidemiology and the complexity of management and political jurisdictions. Tools designed specifically to assess the risk of CWD introduction would be of great value to policy makers in areas where CWD has not been detected.

  7. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

    Science.gov (United States)

    Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

    2017-07-01

    OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

  8. Anonymising the Sparse Dataset: A New Privacy Preservation Approach while Predicting Diseases

    Directory of Open Access Journals (Sweden)

    V. Shyamala Susan

    2016-09-01

    Full Text Available Data mining techniques analyze the medical dataset with the intention of enhancing patient’s health and privacy. Most of the existing techniques are properly suited for low dimensional medical dataset. The proposed methodology designs a model for the representation of sparse high dimensional medical dataset with the attitude of protecting the patient’s privacy from an adversary and additionally to predict the disease’s threat degree. In a sparse data set many non-zero values are randomly spread in the entire data space. Hence, the challenge is to cluster the correlated patient’s record to predict the risk degree of the disease earlier than they occur in patients and to keep privacy. The first phase converts the sparse dataset right into a band matrix through the Genetic algorithm along with Cuckoo Search (GCS.This groups the correlated patient’s record together and arranges them close to the diagonal. The next segment dissociates the patient’s disease, which is a sensitive value (SA with the parameters that determine the disease normally Quasi Identifier (QI.Finally, density based clustering technique is used over the underlying data to  create anonymized groups to maintain privacy and to predict the risk level of disease. Empirical assessments on actual health care data corresponding to V.A.Medical Centre heart disease dataset reveal the efficiency of this model pertaining to information loss, utility and privacy.

  9. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  10. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  11. Predictive risk factors for moderate to severe hyperbilirubinemia

    Directory of Open Access Journals (Sweden)

    Gláucia Macedo de Lima

    2007-12-01

    Full Text Available Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetric and neonatal factors; risk estimationwas based on the odds ratio (95% confidence interval; a bi-variantmultivariate regression logistic analysis was applied to variables forp < 0.1. Results: Of 818 babies born during the studied period, 94(11% had jaundice prior to hospital discharge. Phototherapy was usedon 69 (73% patients. Predictive factors for severity were multiparity;prolonged rupture of membranes, dystocia, cephalohematoma, a lowApgar score, prematurity and small-for-date babies. Following birth,breastfeeding, sepsis, Rh incompatibility, and jaundice presentingbefore the third day of life were associated with an increased risk ofhyperbilirubinemia and the need for therapy. Conclusion: Other thanthose characteristics that are singly associated with phototherapy,we concluded that multiparity, presumed neonatal asphyxia, low birthweight and infection are the main predictive factors leading to moderateand severe jaundice in newborn infants in our neonatal unit.

  12. Knowledge of heart disease risk in a multicultural community sample of people with diabetes.

    Science.gov (United States)

    Wagner, Julie; Lacey, Kimberly; Abbott, Gina; de Groot, Mary; Chyun, Deborah

    2006-06-01

    Prevention of coronary heart disease (CHD) is a primary goal of diabetes management. Unfortunately, CHD risk knowledge is poor among people with diabetes. The objective is to determine predictors of CHD risk knowledge in a community sample of people with diabetes. A total of 678 people with diabetes completed the Heart Disease Facts Questionnaire (HDFQ), a valid and reliable measure of knowledge about the relationship between diabetes and heart disease. In regression analysis with demographics predicting HDFQ scores, sex, annual income, education, and health insurance status predicted HDFQ scores. In a separate regression analysis, having CHD risk factors did not predict HDFQ scores, however, taking medication for CHD risk factors did predict higher HDFQ scores. An analysis of variance showed significant differences between ethnic groups for HDFQ scores; Whites (M = 20.9) showed more CHD risk knowledge than African Americans (M = 19.6), who in turn showed more than Latinos (M = 18.2). Asians scored near Whites (M = 20.4) but did not differ significantly from any other group. Controlling for numerous demographic, socioeconomic, health care, diabetes, and cardiovascular health variables, the magnitude of ethnic differences was attenuated, but persisted. Education regarding modifiable risk factors must be delivered in a timely fashion so that lifestyle modification can be implemented and evaluated before pharmacotherapy is deemed necessary. African Americans and Latinos with diabetes are in the greatest need of education regarding CHD risk.

  13. Prediction of Adult Dyslipidemia Using Genetic and Childhood Clinical Risk Factors: The Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Nuotio, Joel; Pitkänen, Niina; Magnussen, Costan G; Buscot, Marie-Jeanne; Venäläinen, Mikko S; Elo, Laura L; Jokinen, Eero; Laitinen, Tomi; Taittonen, Leena; Hutri-Kähönen, Nina; Lyytikäinen, Leo-Pekka; Lehtimäki, Terho; Viikari, Jorma S; Juonala, Markus; Raitakari, Olli T

    2017-06-01

    Dyslipidemia is a major modifiable risk factor for cardiovascular disease. We examined whether the addition of novel single-nucleotide polymorphisms for blood lipid levels enhances the prediction of adult dyslipidemia in comparison to childhood lipid measures. Two thousand four hundred and twenty-two participants of the Cardiovascular Risk in Young Finns Study who had participated in 2 surveys held during childhood (in 1980 when aged 3-18 years and in 1986) and at least once in a follow-up study in adulthood (2001, 2007, and 2011) were included. We examined whether inclusion of a lipid-specific weighted genetic risk score based on 58 single-nucleotide polymorphisms for low-density lipoprotein cholesterol, 71 single-nucleotide polymorphisms for high-density lipoprotein cholesterol, and 40 single-nucleotide polymorphisms for triglycerides improved the prediction of adult dyslipidemia compared with clinical childhood risk factors. Adjusting for age, sex, body mass index, physical activity, and smoking in childhood, childhood lipid levels, and weighted genetic risk scores were associated with an increased risk of adult dyslipidemia for all lipids. Risk assessment based on 2 childhood lipid measures and the lipid-specific weighted genetic risk scores improved the accuracy of predicting adult dyslipidemia compared with the approach using only childhood lipid measures for low-density lipoprotein cholesterol (area under the receiver-operating characteristic curve 0.806 versus 0.811; P =0.01) and triglycerides (area under the receiver-operating characteristic curve 0.740 versus area under the receiver-operating characteristic curve 0.758; P dyslipidemia in adulthood. © 2017 American Heart Association, Inc.

  14. Cumulative risk hypothesis: Predicting and preventing child maltreatment recidivism.

    Science.gov (United States)

    Solomon, David; Åsberg, Kia; Peer, Samuel; Prince, Gwendolyn

    2016-08-01

    Although Child Protective Services (CPS) and other child welfare agencies aim to prevent further maltreatment in cases of child abuse and neglect, recidivism is common. Having a better understanding of recidivism predictors could aid in preventing additional instances of maltreatment. A previous study identified two CPS interventions that predicted recidivism: psychotherapy for the parent, which was related to a reduced risk of recidivism, and temporary removal of the child from the parent's custody, which was related to an increased recidivism risk. However, counter to expectations, this previous study did not identify any other specific risk factors related to maltreatment recidivism. For the current study, it was hypothesized that (a) cumulative risk (i.e., the total number of risk factors) would significantly predict maltreatment recidivism above and beyond intervention variables in a sample of CPS case files and that (b) therapy for the parent would be related to a reduced likelihood of recidivism. Because it was believed that the relation between temporary removal of a child from the parent's custody and maltreatment recidivism is explained by cumulative risk, the study also hypothesized that that the relation between temporary removal of the child from the parent's custody and recidivism would be mediated by cumulative risk. After performing a hierarchical logistic regression analysis, the first two hypotheses were supported, and an additional predictor, psychotherapy for the child, also was related to reduced chances of recidivism. However, Hypothesis 3 was not supported, as risk did not significantly mediate the relation between temporary removal and recidivism. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Predicting risk and human reliability: a new approach

    International Nuclear Information System (INIS)

    Duffey, R.; Ha, T.-S.

    2009-01-01

    Learning from experience describes human reliability and skill acquisition, and the resulting theory has been validated by comparison against millions of outcome data from multiple industries and technologies worldwide. The resulting predictions were used to benchmark the classic first generation human reliability methods adopted in probabilistic risk assessments. The learning rate, probabilities and response times are also consistent with the existing psychological models for human learning and error correction. The new approach also implies a finite lower bound probability that is not predicted by empirical statistical distributions that ignore the known and fundamental learning effects. (author)

  16. Assessing the risk of Legionnaires' disease: the inhalation exposure model and the estimated risk in residential bathrooms.

    Science.gov (United States)

    Azuma, Kenichi; Uchiyama, Iwao; Okumura, Jiro

    2013-02-01

    Legionella are widely found in the built environment. Patients with Legionnaires' disease have been increasing in Japan; however, health risks from Legionella bacteria in the environment are not appropriately assessed. We performed a quantitative health risk assessment modeled on residential bathrooms in the Adachi outbreak area and estimated risk levels. The estimated risks in the Adachi outbreak approximately corresponded to the risk levels exponentially extrapolated into lower levels on the basis of infection and mortality rates calculated from actual outbreaks, suggesting that the model of Legionnaires' disease in residential bathrooms was adequate to predict disease risk for the evaluated outbreaks. Based on this model, the infection and mortality risk levels per year in 10 CFU/100 ml (100 CFU/L) of the Japanese water quality guideline value were approximately 10(-2) and 10(-5), respectively. However, acceptable risk levels of infection and mortality from Legionnaires' disease should be adjusted to approximately 10(-4) and 10(-7), respectively, per year. Therefore, a reference value of 0.1 CFU/100 ml (1 CFU/L) as a water quality guideline for Legionella bacteria is recommended. This value is occasionally less than the actual detection limit. Legionella levels in water system should be maintained as low as reasonably achievable (<1 CFU/L). Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Development of a flood-induced health risk prediction model for Africa

    Science.gov (United States)

    Lee, D.; Block, P. J.

    2017-12-01

    Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, drinking water, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. In this study, we develop flood-induced health risk prediction model for African regions using season-ahead flood predictions with climate drivers and a variety of physical and socio-economic information, such as local hazard, exposure, resilience, and health vulnerability indicators. Skillful prediction of flood and flood-induced health risks can contribute to practical pre- and post-disaster responses in both local- and global-scales, and may eventually be integrated into multi-hazard early warning systems for informed advanced planning and management. This is especially attractive for areas with limited observations and/or little capacity to develop flood-induced health risk warning systems.

  18. Risk prediction of cardiovascular death based on the QTc interval

    DEFF Research Database (Denmark)

    Nielsen, Jonas B; Graff, Claus; Rasmussen, Peter V

    2014-01-01

    electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Framingham formula was used for heart rate-correction of the QT interval. Data on medication, comorbidity, and outcomes were retrieved from administrative registries. During a median follow-up period of 6......AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital...

  19. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    Science.gov (United States)

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  20. Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers.

    Science.gov (United States)

    Reitz, Christiane; Mayeux, Richard

    2014-04-15

    The global prevalence of dementia is as high as 24 million, and has been predicted to quadruple by the year 2050. In the US alone, Alzheimer disease (AD) - the most frequent cause of dementia characterized by a progressive decline in cognitive function in particular the memory domain - causes estimated health-care costs of $ 172 billion per year. Key neuropathological hallmarks of the AD brain are diffuse and neuritic extracellular amyloid plaques - often surrounded by dystrophic neurites - and intracellular neurofibrillary tangles. These pathological changes are frequently accompanied by reactive microgliosis and loss of neurons, white matter and synapses. The etiological mechanisms underlying these neuropathological changes remain unclear, but are probably caused by both environmental and genetic factors. In this review article, we provide an overview of the epidemiology of AD, review the biomarkers that may be used for risk assessment and in diagnosis, and give suggestions for future research. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. Nonparametric predictive inference for combined competing risks data

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani; Coolen, Frank P.A.

    2014-01-01

    The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

  2. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    Science.gov (United States)

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  3. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  4. Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study.

    Science.gov (United States)

    Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

    2017-04-29

    Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn's disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn's disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn's disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51-82) and specificity of 63% (55-71), with a negative predictive value of 95% (94-97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10-0·89; p=0·0296) but not stricturing complication (1·13, 0·51-2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12-2·57; p=0·0120). When this gene signature was included, the model's specificity improved to 71%. Our findings support the usefulness of risk stratification of paediatric patients with Crohn's disease at diagnosis, and selection of anti-TNFα therapy. Crohn's and Colitis Foundation of America, Cincinnati

  5. Prediction of complicated disease course for children newly diagnosed with Crohn’s disease: a multicentre inception cohort study

    Science.gov (United States)

    Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

    2017-01-01

    Summary Background Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn’s disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. Methods We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn’s disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Findings Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn’s disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51–82) and specificity of 63% (55–71), with a negative predictive value of 95% (94–97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10–0·89; p=0·0296) but not stricturing complication (1·13, 0·51–2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12–2·57; p=0·0120). When this gene signature was included, the model’s specificity improved to 71%. Interpretation Our findings support the usefulness of risk stratification of paediatric patients with Crohn’s disease at diagnosis, and selection of

  6. Disease stage, but not sex, predicts depression and psychological distress in Huntington's disease

    DEFF Research Database (Denmark)

    Dale, Maria; Maltby, John; Shimozaki, Steve

    2016-01-01

    OBJECTIVE: Depression and anxiety significantly affect morbidity in Huntington's disease. Mice. models of Huntington's disease have identified sex differences in mood-like behaviours that vary across disease lifespan, but this interaction has not previously been explored in humans with Huntington......'s disease. However, among certain medical populations, evidence of sex differences in mood across various disease stages has been found, reflecting trends among the general population that women tend to experience anxiety and depression 1.5 to 2 times more than men. The current study examined whether...... disease stage and sex, either separately or as an interaction term, predicted anxiety and depression in Huntington's disease. METHODS: A cross-sectional study of REGISTRY data involving 453 Huntington's disease participants from 12 European countries was undertaken using the Hospital Anxiety...

  7. Risk prediction, safety analysis and quantitative probability methods - a caveat

    International Nuclear Information System (INIS)

    Critchley, O.H.

    1976-01-01

    Views are expressed on the use of quantitative techniques for the determination of value judgements in nuclear safety assessments, hazard evaluation, and risk prediction. Caution is urged when attempts are made to quantify value judgements in the field of nuclear safety. Criteria are given the meaningful application of reliability methods but doubts are expressed about their application to safety analysis, risk prediction and design guidances for experimental or prototype plant. Doubts are also expressed about some concomitant methods of population dose evaluation. The complexities of new designs of nuclear power plants make the problem of safety assessment more difficult but some possible approaches are suggested as alternatives to the quantitative techniques criticized. (U.K.)

  8. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  9. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Lucky eMehra

    2016-03-01

    Full Text Available Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB, caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum. The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early

  10. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

    Science.gov (United States)

    Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

    2016-01-01

    Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of

  11. Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

    Science.gov (United States)

    Neufeld, K. N.; Keinath, A. P.; Gugino, B. K.; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

    2017-11-01

    Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good

  12. Communicating cardiovascular disease risk: an interview study of General Practitioners' use of absolute risk within tailored communication strategies.

    Science.gov (United States)

    Bonner, Carissa; Jansen, Jesse; McKinn, Shannon; Irwig, Les; Doust, Jenny; Glasziou, Paul; McCaffery, Kirsten

    2014-05-29

    Cardiovascular disease (CVD) prevention guidelines encourage assessment of absolute CVD risk - the probability of a CVD event within a fixed time period, based on the most predictive risk factors. However, few General Practitioners (GPs) use absolute CVD risk consistently, and communication difficulties have been identified as a barrier to changing practice. This study aimed to explore GPs' descriptions of their CVD risk communication strategies, including the role of absolute risk. Semi-structured interviews were conducted with a purposive sample of 25 GPs in New South Wales, Australia. Transcribed audio-recordings were thematically coded, using the Framework Analysis method to ensure rigour. GPs used absolute CVD risk within three different communication strategies: 'positive', 'scare tactic', and 'indirect'. A 'positive' strategy, which aimed to reassure and motivate, was used for patients with low risk, determination to change lifestyle, and some concern about CVD risk. Absolute risk was used to show how they could reduce risk. A 'scare tactic' strategy was used for patients with high risk, lack of motivation, and a dismissive attitude. Absolute risk was used to 'scare' them into taking action. An 'indirect' strategy, where CVD risk was not the main focus, was used for patients with low risk but some lifestyle risk factors, high anxiety, high resistance to change, or difficulty understanding probabilities. Non-quantitative absolute risk formats were found to be helpful in these situations. This study demonstrated how GPs use three different communication strategies to address the issue of CVD risk, depending on their perception of patient risk, motivation and anxiety. Absolute risk played a different role within each strategy. Providing GPs with alternative ways of explaining absolute risk, in order to achieve different communication aims, may improve their use of absolute CVD risk assessment in practice.

  13. 459 Preventing Cardiovascular Disease Risk Factors through ...

    African Journals Online (AJOL)

    FIRST LADY

    2011-01-18

    Jan 18, 2011 ... injury. Risk factors may be considered as characteristic indicators ... by examining the cardiovascular risk factors that are related to various forms .... Cross country race, Handball, Jogging, Rope jumping, Running Soccer,.

  14. A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.

    Science.gov (United States)

    Blair, David R; Lyttle, Christopher S; Mortensen, Jonathan M; Bearden, Charles F; Jensen, Anders Boeck; Khiabanian, Hossein; Melamed, Rachel; Rabadan, Raul; Bernstam, Elmer V; Brunak, Søren; Jensen, Lars Juhl; Nicolae, Dan; Shah, Nigam H; Grossman, Robert L; Cox, Nancy J; White, Kevin P; Rzhetsky, Andrey

    2013-09-26

    Although countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By mining the medical records of over 110 million patients, we examine the extent to which Mendelian variation contributes to complex disease risk. We detect thousands of associations between Mendelian and complex diseases, revealing a nondegenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci. Using genome-wide association results, we demonstrate that common variants associated with complex diseases are enriched in the genes indicated by this "Mendelian code." Finally, we detect hundreds of comorbidity associations among Mendelian disorders, and we use probabilistic genetic modeling to demonstrate that Mendelian variants likely contribute nonadditively to the risk for a subset of complex diseases. Overall, this study illustrates a complementary approach for mapping complex disease loci and provides unique predictions concerning the etiologies of specific diseases. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Directory of Open Access Journals (Sweden)

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  16. Coronary artery disease risk assessment from unstructured electronic health records using text mining.

    Science.gov (United States)

    Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

    2015-12-01

    Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Elevated Serum Pesticide Levels and Risk of Parkinson Disease

    Science.gov (United States)

    Richardson, Jason R.; Shalat, Stuart L.; Buckley, Brian; Winnik, Bozena; O’Suilleabhain, Padraig; Diaz-Arrastia, Ramon; Reisch, Joan; German, Dwight C.

    2012-01-01

    Background Exposure to pesticides has been reported to increase the risk of Parkinson disease (PD), but identification of the specific pesticides is lacking. Three studies have found elevated levels of organochlorine pesticides in postmortem PD brains. Objective To determine whether elevated levels of organochlorine pesticides are present in the serum of patients with PD. Design Case-control study. Setting An academic medical center. Participants Fifty patients with PD, 43 controls, and 20 patients with Alzheimer disease. Main Outcome Measures Levels of 16 organochlorine pesticides in serum samples. Results β-Hexachlorocyclohexane (β-HCH) was more often detectable in patients with PD (76%) compared with controls (40%) and patients with Alzheimer disease (30%). The median level of β-HCH was higher in patients with PD compared with controls and patients with Alzheimer disease. There were no marked differences in detection between controls and patients with PD concerning any of the other 15 organochlorine pesticides. Finally, we observed a significant odds ratio for the presence of β-HCH in serum to predict a diagnosis of PD vs control (odds ratio, 4.39; 95% confidence interval, 1.67–11.6) and PD vs Alzheimer disease (odds ratio, 5.20), which provides further evidence for the apparent association between serum β-HCH and PD. Conclusions These data suggest that β-HCH is associated with a diagnosis of PD. Further research is warranted regarding the potential role of β-HCH as a etiologic agent for some cases of PD. PMID:19597089

  18. Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

    Directory of Open Access Journals (Sweden)

    Kimmel Marek

    2011-05-01

    Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

  19. Psychodynamic theory and counseling in predictive testing for Huntington's disease.

    Science.gov (United States)

    Tassicker, Roslyn J

    2005-04-01

    This paper revisits psychodynamic theory, which can be applied in predictive testing counseling for Huntington's Disease (HD). Psychodynamic theory has developed from the work of Freud and places importance on early parent-child experiences. The nature of these relationships, or attachments are reflected in adult expectations and relationships. Two significant concepts, identification and fear of abandonment, have been developed and expounded by the psychodynamic theorist, Melanie Klein. The processes of identification and fear of abandonment can become evident in predictive testing counseling and are colored by the client's experience of growing up with a parent affected by Huntington's Disease. In reflecting on family-of-origin experiences, clients can also express implied expectations of the future, and future relationships. Case examples are given to illustrate the dynamic processes of identification and fear of abandonment which may present in the clinical setting. Counselor recognition of these processes can illuminate and inform counseling practice.

  20. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...

  1. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    Science.gov (United States)

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  2. Alcohol dependence and risk of somatic diseases and mortality

    DEFF Research Database (Denmark)

    Holst, Charlotte; Tolstrup, Janne Schurmann; Sørensen, Holger Jelling

    2017-01-01

    AIMS: To (1) estimate sex-specific risks of a comprehensive spectrum of somatic diseases in alcohol-dependent individuals versus a control population, and in the same population to (2) estimate sex-specific risks of dying from the examined somatic diseases. DESIGN: Register-based matched cohort...

  3. Increased Risk of Gallstone Disease Following Colectomy for Ulcerative Colitis

    DEFF Research Database (Denmark)

    Mark-Christensen, Anders; Brandsborg, Søren; Laurberg, Søren

    2017-01-01

    Objectives:Biochemical studies suggest that patients who have had a colectomy or restorative proctocolectomy with ileal pouch-anal anastomosis (IPAA) are at an increased risk of developing gallstone disease, but epidemiological studies are lacking. We evaluated the risk of gallstone disease follo...

  4. Genetically elevated bilirubin and risk of ischaemic heart disease

    DEFF Research Database (Denmark)

    Stender, Stefan; Frikke-Schmidt, R; Nordestgaard, B G

    2013-01-01

    Elevated plasma levels of bilirubin, an endogenous antioxidant, have been associated with reduced risk of ischaemic heart disease (IHD) and myocardial infarction (MI). Whether this is a causal relationship remains unclear.......Elevated plasma levels of bilirubin, an endogenous antioxidant, have been associated with reduced risk of ischaemic heart disease (IHD) and myocardial infarction (MI). Whether this is a causal relationship remains unclear....

  5. Vegetarian diet as a risk factor for symptomatic gallstone disease.

    Science.gov (United States)

    McConnell, T J; Appleby, P N; Key, T J

    2017-06-01

    Previous small studies have shown either no difference or a lower risk of symptomatic gallstone disease in vegetarians than in non-vegetarians. This study examined the incidence of symptomatic gallstone disease in a cohort of British vegetarians and non-vegetarians, and investigated the associations between nutrient intake and risk of symptomatic gallstone disease. The data were analysed from 49 652 adults enroled in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford study, one-third of whom were vegetarian. The linked databases of hospital records were used to identify incident cases. Risk by diet group was estimated using Cox proportional hazards models. Further analysis quantified risk by intakes of selected macronutrients. There were 1182 cases of symptomatic gallstone disease during 687 822 person-years of follow-up (mean=13.85 years). There was a large significant association between increasing body mass index (BMI) and risk of developing symptomatic gallstone disease (overall trend Pvegetarians had a moderately increased risk compared with non-vegetarians (HR: 1.22; 95% CI: 1.06-1.41; P=0.006). Although starch consumption was positively associated with gallstones risk (P=0.002 for trend), it did not explain the increased risk in vegetarians. There is a highly significant association of increased BMI with risk of symptomatic gallstone disease. After adjusting for BMI, there is a small but statistically significant positive association between vegetarian diet and symptomatic gallstone disease.

  6. Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

    Directory of Open Access Journals (Sweden)

    Suchithra Naish

    Full Text Available BACKGROUND: Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV disease under climate change scenarios in Queensland, Australia. METHODS/PRINCIPAL FINDINGS: We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall, socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. CONCLUSIONS/SIGNIFICANCE: We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

  7. Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

    Science.gov (United States)

    Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

    2013-01-01

    Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

  8. Prediction of health risks from accidents: A comprehensive assessment methodology

    International Nuclear Information System (INIS)

    MacFarlane, D.R.; Yuan, Y.C.

    1992-01-01

    We have developed two computer programs to predict radiation risks to individuals and/or the collective population from exposures to accidental releases of radioactive materials. When used together, these two codes provide a consistent, comprehensive tool to estimate not only the risks to specific individuals but also the distribution of risks in the exposed population and the total number of individuals within a specific level of risk. Prompt and latent fatalities are estimated for the exposed population, and from these, the risk to an average individual can be derived. Uncertainty in weather conditions is considered by estimating both the ''median'' and the ''maximum'' population doses based on the frequency distribution of wind speeds and stabilities for a given site. The importance of including all dispersible particles (particles smaller than about 100 μm) for dose and health risk analyses from nonfiltered releases for receptor locations within about 10 km from a release has been investigated. The dose contribution of the large particles (> 10 μm) has been shown to be substantially greater than those from the small particles for the dose receptors in various release and exposure conditions. These conditions include, particularly, elevated releases, strong wind weather, and exposure pathways associated with ground-deposited material over extended periods of time

  9. Predicting the short-term risk of diabetes in HIV-positive patients

    DEFF Research Database (Denmark)

    Petoumenos, Kathy; Worm, Signe W; Fontas, Eric

    2012-01-01

    HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other...... glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

  10. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  11. Autoimmune disease and risk for Parkinson disease A population-based case-control study

    DEFF Research Database (Denmark)

    Rugbjerg, K.; Friis, S.; Ritz, B.

    2009-01-01

    Objective: Inflammatory mediators are increased in autoimmune diseases and may activate microglia and might cause an inflammatory state and degeneration of dopaminergic neurons in the brain. Thus, we evaluated whether having an autoimmune disease increases the risk for developing Parkinson disease...... do not support the hypothesis that autoimmune diseases increase the risk for Parkinson disease. The decreased risk observed among patients with rheumatoid arthritis might be explained by underdiagnosis of movement disorders such as Parkinson disease in this patient group or by a protective effect...

  12. Male Infertility and Risk of Nonmalignant Chronic Diseases

    DEFF Research Database (Denmark)

    Glazer, Clara Helene; Bonde, Jens Peter; Eisenberg, Michael L.

    2017-01-01

    The association between male infertility and increased risk of certain cancers is well studied. Less is known about the long-term risk of nonmalignant diseases in men with decreased fertility. A systemic literature review was performed on the epidemiologic evidence of male infertility...... as a precursor for increased risk of diabetes, cardiovascular diseases, and all-cause mortality. PubMed and Embase were searched from January 1, 1980, to September 1, 2016, to identify epidemiological studies reporting associations between male infertility and the outcomes of interest. Animal studies, case...... prospective (three on risk of mortality, one on risk of chronic diseases) and three were cross-sectional relating male infertility to the Charlson Comorbidity Index. The current epidemiological evidence is compatible with an association between male infertility and risk of chronic disease and mortality...

  13. Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients

    DEFF Research Database (Denmark)

    Brorsson, Caroline A; Nielsen, Lotte B; Andersen, Marie-Louise

    2016-01-01

    Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type...... 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease...... constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several...

  14. Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases.

    Directory of Open Access Journals (Sweden)

    Rong Chen

    Full Text Available Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may

  15. [Analysis of 14 individuals who requested predictive genetic testing for hereditary neuromuscular diseases].

    Science.gov (United States)

    Yoshida, Kunihiro; Tamai, Mariko; Kubota, Takeo; Kawame, Hiroshi; Amano, Naoji; Ikeda, Shu-ichi; Fukushima, Yoshimitsu

    2002-02-01

    Predictive genetic testing for hereditary neuromuscular diseases is a delicate issue for individuals at risk and their families, as well as for medical staff because these diseases are often late-onset and intractable. Therefore careful pre- and post-test genetic counseling and psychosocial support should be provided along with such genetic testing. The Division of Clinical and Molecular Genetics was established at our hospital in May 1996 to provide skilled professional genetic counseling. Since its establishment, 14 individuals have visited our clinic to request predictive genetic testing for hereditary neuromuscular diseases (4 for myotonic dystrophy, 6 for spinocerebellar ataxia, 3 for Huntington's disease, and 1 for Alzheimer's disease). The main reasons for considering testing were to remove uncertainty about the genetic status and to plan for the future. Nine of 14 individuals requested testing for making decisions about a forthcoming marriage or pregnancy (family planning). Other reasons raised by the individuals included career or financial planning, planning for their own health care, and knowing the risk for their children. At the first genetic counseling session, all of the individuals expressed hopes of not being a gene carrier and of escaping from fear of disease, and seemed not to be mentally well prepared for an increased-risk result. To date, 7 of the 14 individuals have received genetic testing and only one, who underwent predictive genetic testing for spinocerebellar ataxia, was given an increased-risk result. The seven individuals including the one with an increased-risk result, have coped well with their new knowledge about their genetic status after the testing results were disclosed. None of them has expressed regret. In pre-test genetic counseling sessions, we consider it quite important not only to determine the psychological status of the individual, but also to make the individual try to anticipate the changes in his/her life upon

  16. Screening for nutritional risk in hospitalized children with liver disease.

    Science.gov (United States)

    Song, Tiantian; Mu, Ying; Gong, Xue; Ma, Wenyan; Li, Li

    2017-01-01

    Malnutrition is a major contributor to morbidity and mortality from pediatric liver disease. We investigated the prevalence of both malnutrition and high nutritional risk in hospitalized children with liver disease as well as the rate of in-hospital nutritional support. A total of 2,874 hospitalized children and adolescents with liver disease aged 1 to 17 years (inclusive) were enrolled. Malnutrition was screened by anthropometric measures (height-for-age, weight-for-height, weight-for-age, and BMI- for-age z-scores). The Screening Tool for Risk on Nutritional Status and Growth (STRONGkids) was used to evaluate nutritional risk status. Nutrition markers in blood, rate of nutritional support, length of hospital stay, and hospital fees were compared among nutritional risk groups. The overall prevalence of malnutrition was 38.6%. About 20.0% of children had high nutritional risk, and prevalence of malnutrition was markedly greater in the high nutritional risk group compared with the moderate risk group (67.9% vs 31.3%). Serum albumin and prealbumin differed significantly between high and moderate risk groups (pnutritional risk and 3.5% with moderate nutritional risk received nutrition support during hospitalization. Children with high nutritional risk had longer hospital stays and greater hospital costs (pnutritional risk is also prevalent at admission. Albumin and prealbumin are sensitive markers for distinguishing nutritional risk groups. High nutritional risk prolongs length of stay and increases hospital costs. The nutritional support rate is still low and requires standardization.

  17. Iatrogenic disease in the elderly: risk factors, consequences, and prevention

    Directory of Open Access Journals (Sweden)

    Sompol Permpongkosol

    2011-03-01

    Full Text Available Sompol PermpongkosolDivision of Urology, Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, ThailandAbstract: The epidemiology of iatrogenic disease in the elderly has not been extensively reported. Risk factors of iatrogenic disease in the elderly are drug-induced iatrogenic disease, multiple chronic diseases, multiple physicians, hospitalization, and medical or surgical procedures. Iatrogenic disease can have a great psychomotor impact and important social consequences. To identify patients at high risk is the first step in prevention as most of the iatrogenic diseases are preventable. Interventions that can prevent iatrogenic complications include specific interventions, the use of a geriatric interdisciplinary team, pharmacist consultation and acute care for the elderly units.Keywords: iatrogenic disease, elderly, risk factors, prevention

  18. Incidence and risk factors of Parkinson's disease in The Netherlands.

    NARCIS (Netherlands)

    Hofman, A.; Collette, H.J.A.; Bartelds, A.I.M.

    1989-01-01

    The incidence and some risk factors of Parkinson's disease were investigated in a study performed in The Netherlands. The study was based on a disease register of the Sentinel Stations, which provide a complete ascertainment of new patients with Parkinson's disease in 60 general practices in The

  19. SPATIAL DYNAMICS OF LAND COVER AND INFECTIOUS DISEASE RISK

    Science.gov (United States)

    Climate changes may allow for vector-transmitted tropical diseases to spread into temperate areas. Areas of low ecological diversity are at higher risk of infectious disease transmission due to decreased zooprophylaxis, the diversion of disease carrying insects from humans to...

  20. A Novel Risk Scoring System Reliably Predicts Readmission Following Pancreatectomy

    Science.gov (United States)

    Valero, Vicente; Grimm, Joshua C.; Kilic, Arman; Lewis, Russell L.; Tosoian, Jeffrey J.; He, Jin; Griffin, James; Cameron, John L.; Weiss, Matthew J.; Vollmer, Charles M.; Wolfgang, Christopher L.

    2015-01-01

    Background Postoperative readmissions have been proposed by Medicare as a quality metric and may impact provider reimbursement. Since readmission following pancreatectomy is common, we sought to identify factors associated with readmission in order to establish a predictive risk scoring system (RSS). Study Design A retrospective analysis of 2,360 pancreatectomies performed at nine, high-volume pancreatic centers between 2005 and 2011 was performed. Forty-five factors strongly associated with readmission were identified. To derive and validate a RSS, the population was randomly divided into two cohorts in a 4:1 fashion. A multivariable logistic regression model was constructed and scores were assigned based on the relative odds ratio of each independent predictor. A composite Readmission After Pancreatectomy (RAP) score was generated and then stratified to create risk groups. Results Overall, 464 (19.7%) patients were readmitted within 90-days. Eight pre- and postoperative factors, including prior myocardial infarction (OR 2.03), ASA Class ≥ 3 (OR 1.34), dementia (OR 6.22), hemorrhage (OR 1.81), delayed gastric emptying (OR 1.78), surgical site infection (OR 3.31), sepsis (OR 3.10) and short length of stay (OR 1.51), were independently predictive of readmission. The 32-point RAP score generated from the derivation cohort was highly predictive of readmission in the validation cohort (AUC 0.72). The low (0-3), intermediate (4-7) and high risk (>7) groups correlated to 11.7%, 17.5% and 45.4% observed readmission rates, respectively (preadmission following pancreatectomy. Identification of patients with increased risk of readmission using the RAP score will allow efficient resource allocation aimed to attenuate readmission rates. It also has potential to serve as a new metric for comparative research and quality assessment. PMID:25797757

  1. Prediction of fibre architecture and adaptation in diseased carotid bifurcations.

    LENUS (Irish Health Repository)

    Creane, Arthur

    2011-12-01

    Many studies have used patient-specific finite element models to estimate the stress environment in atherosclerotic plaques, attempting to correlate the magnitude of stress to plaque vulnerability. In complex geometries, few studies have incorporated the anisotropic material response of arterial tissue. This paper presents a fibre remodelling algorithm to predict the fibre architecture, and thus anisotropic material response in four patient-specific models of the carotid bifurcation. The change in fibre architecture during disease progression and its affect on the stress environment in the plaque were predicted. The mean fibre directions were assumed to lie at an angle between the two positive principal strain directions. The angle and the degree of dispersion were assumed to depend on the ratio of principal strain values. Results were compared with experimental observations and other numerical studies. In non-branching regions of each model, the typical double helix arterial fibre pattern was predicted while at the bifurcation and in regions of plaque burden, more complex fibre architectures were found. The predicted change in fibre architecture in the arterial tissue during plaque progression was found to alter the stress environment in the plaque. This suggests that the specimen-specific anisotropic response of the tissue should be taken into account to accurately predict stresses in the plaque. Since determination of the fibre architecture in vivo is a difficult task, the system presented here provides a useful method of estimating the fibre architecture in complex arterial geometries.

  2. Assessment of Cardiovascular Disease Risk in South Asian Populations

    Directory of Open Access Journals (Sweden)

    S. Monira Hussain

    2013-01-01

    Full Text Available Although South Asian populations have high cardiovascular disease (CVD burden in the world, their patterns of individual CVD risk factors have not been fully studied. None of the available algorithms/scores to assess CVD risk have originated from these populations. To explore the relevance of CVD risk scores for these populations, literature search and qualitative synthesis of available evidence were performed. South Asians usually have higher levels of both “classical” and nontraditional CVD risk factors and experience these at a younger age. There are marked variations in risk profiles between South Asian populations. More than 100 risk algorithms are currently available, with varying risk factors. However, no available algorithm has included all important risk factors that underlie CVD in these populations. The future challenge is either to appropriately calibrate current risk algorithms or ideally to develop new risk algorithms that include variables that provide an accurate estimate of CVD risk.

  3. Japanese scoring systems to predict resistance to intravenous immunoglobulin in Kawasaki disease were unreliable for Caucasian Israeli children.

    Science.gov (United States)

    Arane, Karen; Mendelsohn, Kerry; Mimouni, Michael; Mimouni, Francis; Koren, Yael; Simon, Dafna Brik; Bahat, Hilla; Helou, Mona Hanna; Mendelson, Amir; Hezkelo, Nofar; Glatstein, Miguel; Berkun, Yackov; Eisenstein, Eli; Aviel, Yonatan Butbul; Brik, Riva; Hashkes, Philip J; Uziel, Yosef; Harel, Liora; Amarilyo, Gil

    2018-05-24

    This study assessed the validity of using established Japanese risk scoring methods to predict intravenous immunoglobulin (IVIG) resistance to Kawasaki disease in Israeli children. We reviewed the medical records of 282 patients (70% male) with Kawasaki disease from six Israeli medical centres between 2004-2013. Their mean age was 2.5 years. The risk scores were calculated using the Kobayashi, Sano and Egami scoring methods and analysed to determine if a higher risk score predicted IVIG resistance in this population. Factors that predicted a lack of response to the initial IVIG dose were identified. We found that 18% did not respond to the first IVIG dose. The three scoring methods were unable to reliably predict IVIG resistance, with sensitivities of 23-32% and specificities of 67-87%. Calculating a predictive score that was specific for this population was also unsuccessful. The factors that predicted a lacked of response to the first IVIG dose included low albumin, elevated total bilirubin and ethnicity. The established risk scoring methods created for Japanese populations with Kawasaki disease were not suitable for predicting IVIG resistance in Caucasian Israeli children and we were unable to create a specific scoring method that was able to do this. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  4. Hypoalbuminaemia predicts outcome in adult patients with congenital heart disease

    Science.gov (United States)

    Kempny, Aleksander; Diller, Gerhard-Paul; Alonso-Gonzalez, Rafael; Uebing, Anselm; Rafiq, Isma; Li, Wei; Swan, Lorna; Hooper, James; Donovan, Jackie; Wort, Stephen J; Gatzoulis, Michael A; Dimopoulos, Konstantinos

    2015-01-01

    Background In patients with acquired heart failure, hypoalbuminaemia is associated with increased risk of death. The prevalence of hypoproteinaemia and hypoalbuminaemia and their relation to outcome in adult patients with congenital heart disease (ACHD) remains, however, unknown. Methods Data on patients with ACHD who underwent blood testing in our centre within the last 14 years were collected. The relation between laboratory, clinical or demographic parameters at baseline and mortality was assessed using Cox proportional hazards regression analysis. Results A total of 2886 patients with ACHD were included. Mean age was 33.3 years (23.6–44.7) and 50.1% patients were men. Median plasma albumin concentration was 41.0 g/L (38.0–44.0), whereas hypoalbuminaemia (disease complexity, hypoalbuminaemia remained a significant predictor of death. Conclusions Hypoalbuminaemia is common in patients with ACHD and is associated with a threefold increased risk of risk of death. Hypoalbuminaemia, therefore, should be included in risk-stratification algorithms as it may assist management decisions and timing of interventions in the growing ACHD population. PMID:25736048

  5. SURVEYING THE RISKS FROM EMERGING DISEASES

    Science.gov (United States)

    Despite advances in sanitation and public health, new waterborne diseases have continued to cause outbreaks in humans. The reason why these organisms can cause disease outbreaks, is that their biology allows them to circumvent the safeguards put in place to prevent transmission ...

  6. Increased brain-predicted aging in treated HIV disease.

    Science.gov (United States)

    Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

    2017-04-04

    To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

  7. The Impact of Personalized Risk Feedback on Mexican Americans' Perceived Risk for Heart Disease and Diabetes

    Science.gov (United States)

    Hovick, Shelly R.; Wilkinson, Anna V.; Ashida, Sato; de Heer, Hendrik D.; Koehly, Laura M.

    2014-01-01

    Little is known about the effect of personalized risk information on risk perceptions over time, particularly among ethnically diverse subpopulations. The present study examines Mexican American's (MAs) risk perceptions for heart disease and diabetes at baseline and following receipt of risk feedback based on family health history. Participants…

  8. Advanced echocardiography and clinical surrogates to risk stratify and manage patients with structural heart disease

    NARCIS (Netherlands)

    Debonnaire, Philippe Jean Marc Rita

    2016-01-01

    Part I focuses on the potential role of 3-dimensional echocardiography. At first a clinical risk score model for prediction of outcome in patients undergoing TAVI is presented (Chapter 2). Second the role of 3D-echocardiography is explored in depth in patients with mitral valve disease. Different

  9. Identifying fallers with Parkinson's disease using home-based tests: who is at risk?

    NARCIS (Netherlands)

    Lim-de Vries, L.I.I.K.; van Wegen, E.E.; Jones, D.; Rochester, L.; Nieuwboer, A.; Willems, A.M.; Baker, K.; Hetherington, V.; Kwakkel, G.

    2008-01-01

    The objective of this work is to determine risk factors for falling in patients with Parkinson's disease (PD) using home-based assessments and develop a prediction model. Data on falls, balance, gait-related activities, and nonmotor symptoms were obtained from 153 PD patients (Hoehn-Yahr 2-4) in

  10. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    Science.gov (United States)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  11. Threat and error management for anesthesiologists: a predictive risk taxonomy

    Science.gov (United States)

    Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

    2015-01-01

    Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

  12. Hemoglobin and hematocrit levels in the prediction of complicated Crohn's disease behavior--a cohort study.

    Science.gov (United States)

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network "Kompetenznetz CED" were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior.

  13. Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

    Science.gov (United States)

    Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

    2017-12-01

    Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

  14. Update on breast cancer risk prediction and prevention.

    Science.gov (United States)

    Sestak, Ivana; Cuzick, Jack

    2015-02-01

    Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

  15. Orchid Classification Disease Identification And Healthiness Prediction System

    Directory of Open Access Journals (Sweden)

    K. W. V Sanjaya

    2015-03-01

    Full Text Available Abstract Floriculture has become one of Sri Lankas major foreign exchange ventures and it has grown substantially during the last few years. Currently we can find three major types of growers in floriculture. They are Large Commercial Ventures Middle Level growers and Village Level growers. Both Middle Level and Village level growers usually go for low cost cultivation with minimum advanced techniques sticking to conventional methods. Orchid cultivation is more pleasurable and profitable than any other floriculture ventures. As the orchid cultivation is so pleasurable we can introduce another group of growers who cultivate orchid in their home gardens for making their home gardens beautiful. But the problem is that most of these growers may not have the knowledge to identify the specie of the plants as there are a number of similar looking plants which are in different species. And also they may not have the knowledge about the orchid diseases. Because of that they may not be able to get the maximum outcome from their cultivations. So the aim of our project is to address the above mentioned issues by introducing a system which can identify orchid species amp diseases and predict the healthiness of the orchid plants. The only input to this system is an image of an orchid leaf and the system will provide the orchid specie name diseases if there any healthiness of the orchid plant and suggestions to overcome the issues associated with the orchid plant as the output. We identify the orchid species and diseases by extracting the features of orchid plant leaf in the input image using image processing technics and with the use of data mining technics we predict the healthiness of the orchid plant. So this system will be a great help for the people who love to grow orchids but dont have knowledge about the orchid species and diseases. And also they will be able to find the healthiness of their orchid plants.

  16. Predicting changes in hypertension control using electronic health records from a chronic disease management program

    Science.gov (United States)

    Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

    2014-01-01

    Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans. PMID:24045907

  17. Predicting changes in hypertension control using electronic health records from a chronic disease management program.

    Science.gov (United States)

    Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

    2014-01-01

    Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.

  18. Asymptomatic Extracranial Artery Stenosis and the Risk of Cardiovascular and Cerebrovascular Diseases

    OpenAIRE

    Wang, Dandan; Wang, Jing; Jin, Cheng; Ji, Ruijun; Wang, Anxin; Li, Xin; Gao, Xiang; Wu, Shouling; Zhou, Yong; Zhao, Xingquan

    2016-01-01

    Asymptomatic extracranial artery stenosis (ECAS) is a well-known risk factor for stroke events, but it remains unclear whether it has the same role in predicting cardiovascular and cerebrovascular diseases, especially in China. We investigated the potential associations between ECAS, carotid plaque and carotid intima-media thickness and the new occurrence of cardiovascular and cerebrovascular diseases in the study. Out of 5440 study participants, 364 showed an asymptomatic ECAS at baseline, a...

  19. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    Science.gov (United States)

    Holmlund, A; Lampa, E; Lind, L

    2017-07-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P 4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  20. Inflammatory bowel disease and risk of Parkinson's disease in Medicare beneficiaries.

    Science.gov (United States)

    Camacho-Soto, Alejandra; Gross, Anat; Searles Nielsen, Susan; Dey, Neelendu; Racette, Brad A

    2018-05-01

    Gastrointestinal (GI) dysfunction precedes the motor symptoms of Parkinson's disease (PD) by several years. PD patients have abnormal aggregation of intestinal α-synuclein, the accumulation of which may be promoted by inflammation. The relationship between intestinal α-synuclein aggregates and central nervous system neuropathology is unknown. Recently, we observed a possible inverse association between inflammatory bowel disease (IBD) and PD as part of a predictive model of PD. Therefore, the objective of this study was to examine the relationship between PD risk and IBD and IBD-associated conditions and treatment. Using a case-control design, we identified 89,790 newly diagnosed PD cases and 118,095 population-based controls >65 years of age using comprehensive Medicare data from 2004-2009 including detailed claims data. We classified IBD using International Classification of Diseases version 9 (ICD-9) diagnosis codes. We used logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the association between PD and IBD. Covariates included age, sex, race/ethnicity, smoking, Elixhauser comorbidities, and health care use. PD was inversely associated with IBD overall (OR = 0.85, 95% CI 0.80-0.91) and with both Crohn's disease (OR = 0.83, 95% CI 0.74-0.93) and ulcerative colitis (OR = 0.88, 95% CI 0.82-0.96). Among beneficiaries with ≥2 ICD-9 codes for IBD, there was an inverse dose-response association between number of IBD ICD-9 codes, as a potential proxy for IBD severity, and PD (p-for-trend = 0.006). IBD is associated with a lower risk of developing PD. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Risk of neurological diseases among survivors of electric shocks

    DEFF Research Database (Denmark)

    Grell, Kathrine; Meersohn, Andrea; Schüz, Joachim

    2012-01-01

    Several studies suggest a link between electric injuries and neurological diseases, where electric shocks may explain elevated risks for neuronal degeneration and, subsequently, neurological diseases. We conducted a retrospective cohort study on the risk of neurological diseases among people...... in Denmark who had survived an electric accident in 1968-2008. The cohort included 3,133 people and occurrences of neurological diseases were determined by linkage to the nationwide population-based Danish National Register of Patients. The numbers of cases observed at first hospital contact in the cohort...... were compared with the respective rates of first hospital contacts for neurological diseases in the general population. We observed significantly increased risks for peripheral nerve diseases (standardized hospitalization ratio (SHR), 1.66; 95% confidence interval (CI), 1.22-2.22), for migraine (SHR, 1...

  2. Adolescent expectations of early death predict adult risk behaviors.

    Directory of Open Access Journals (Sweden)

    Quynh C Nguyen

    Full Text Available Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II, 2001-02 (Wave III and 2008 (Wave IV; ages 24-32. Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  3. Risk of bleeding related to antithrombotic treatment in cardiovascular disease

    DEFF Research Database (Denmark)

    Sørensen, Rikke; Olesen, Jonas B; Charlot, Mette

    2012-01-01

    Antithrombotic therapy is a cornerstone of treatment in patients with cardiovascular disease with bleeding being the most feared complication. This review describes the risk of bleeding related to different combinations of antithrombotic drugs used for cardiovascular disease: acute coronary...... syndrome (ACS), atrial fibrillation (AF), cerebrovascular (CVD) and peripheral arterial disease (PAD). Different risk assessment schemes and bleeding definitions are compared. The HAS-BLED risk score is recommended in patients with AF and in ACS patients with AF. In patients with ACS with or without...

  4. Postmenopausal Estrogen Therapy and Risk of Gallstone Disease

    DEFF Research Database (Denmark)

    Simonsen, Maja Hellfritzsch; Erichsen, Rune; Frøslev, Trine

    2013-01-01

    BACKGROUND: Female gender and increasing age are key risk factors for gallstone disease; therefore, postmenopausal women are at high risk. Estrogen increases cholesterol saturation of bile and may further increase gallstone risk, but population-based evidence is sparse. OBJECTIVE: Our objective......, and parity. RESULTS: We identified 16,386 cases with gallstone disease and 163,860 controls. A total of 1,425 cases (8.7 %) and 8,930 controls (5.4 %) were current estrogen users, yielding an adjusted OR for gallstone disease of 1.74 (95 % CI 1.64-1.85) compared with non-users. The corresponding adjusted...

  5. Management of Cardiovascular Risk in Patients with Chronic Inflammatory Diseases

    DEFF Research Database (Denmark)

    Lindhardsen, Jesper; Kristensen, Søren Lund; Ahlehoff, Ole

    2016-01-01

    An increased risk of cardiovascular disease (CVD) has been observed in a range of chronic inflammatory diseases (CID), including rheumatoid arthritis (RA), psoriasis, inflammatory bowel diseases (IBD), and systemic lupus erythematosus (SLE). The increased risk of CVDs and reduced life expectancy...... considerable interest in recent years. We briefly summarize the current level of evidence of the association between CIDs and CVD and cardiovascular risk management recommendations. Perspectives of ongoing and planned trials are discussed in consideration of potential ways to improve primary and secondary CVD...

  6. Incidence, Risk and Prognosis of Parkinson Disease

    NARCIS (Netherlands)

    L.M.L. de Lau (Lonneke)

    2006-01-01

    textabstractParkinson disease (PD) is the second most common neurodegenerative disorder, and is clinically characterized by resting tremor, rigidity, bradykinesia and postural imbalance. These typical motor symptoms result from a selective degeneration of dopamine-producing neurons in the

  7. Association of chest pain and risk of cardiovascular disease with coronary atherosclerosis in patients with inflammatory joint diseases

    Directory of Open Access Journals (Sweden)

    Silvia eRollefstad

    2015-11-01

    Full Text Available Objectives: The relation between chest pain and coronary atherosclerosis (CA in patients with inflammatory joint diseases (IJD has not been explored previously. Our aim was to evaluate the associations of the presence of chest pain and the predicted 10-year risk of cardiovascular disease (CVD by use of several CVD risk algorithms, with multi-detector computer tomography (MDCT coronary angiography verified CA. Methods: Detailed information concerning chest pain and CVD risk factors was obtained in 335 patients with rheumatoid arthritis (RA and ankylosing spondylitis (AS. In addition, 119 of these patients underwent MDCT coronary angiography.Results: Thirty-one percent of the patients (104/335 reported chest pain. Only 6 patients (1.8% had atypical angina pectoris (pricking pain at rest. In 69 patients without chest pain, two thirds had CA, while in those who reported chest pain (n=50, CA was present in 48.0%. In a logistic regression analysis, chest pain was not associated with CA (dependent variable (p=0.43. About 30% (Nagelkerke R2 of CA was explained by any of the CVD risk calculators: SCORE, Framingham Risk Score or Reynolds Risk Score.Conclusion: The presence of chest pain was surprisingly infrequently reported in patients with IJD who were referred for a CVD risk evaluation. However, when present, chest pain was weakly associated with CA, in contrast to the predicted CVD risk by several risk calculators which was highly associated with the presence of CA. These findings suggest that clinicians treating patients with IJD should be alert of coronary atherosclerotic disease also in absence of chest pain symptoms.

  8. The Veterans Affairs Cardiac Risk Score: Recalibrating the Atherosclerotic Cardiovascular Disease Score for Applied Use.

    Science.gov (United States)

    Sussman, Jeremy B; Wiitala, Wyndy L; Zawistowski, Matthew; Hofer, Timothy P; Bentley, Douglas; Hayward, Rodney A

    2017-09-01

    Accurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD. We created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality. For the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable. We found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.

  9. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

    Directory of Open Access Journals (Sweden)

    Jaroslav Bendl

    2014-01-01

    Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

  10. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    Science.gov (United States)

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Thigh circumference and risk of heart disease and premature death

    DEFF Research Database (Denmark)

    Heitmann, Berit L; Frederiksen, Peder

    2009-01-01

    of follow-up for total death. RESULTS: A small thigh circumference was associated with an increased risk of cardiovascular and coronary heart diseases and total mortality in both men and women. A threshold effect for thigh circumference was evident, with greatly increased risk of premature death below...... circumference seems to be associated with an increased risk of developing heart disease or premature death. The adverse effects of small thighs might be related to too little muscle mass in the region. The measure of thigh circumference might be a relevant anthropometric measure to help general practitioners...... in early identification of individuals at an increased risk of premature morbidity and mortality....

  12. MMP-7 is a predictive biomarker of disease progression in patients with idiopathic pulmonary fibrosis

    Directory of Open Access Journals (Sweden)

    Yasmina Bauer

    2017-03-01

    Full Text Available Idiopathic pulmonary fibrosis (IPF is a progressive interstitial lung disease with poor prognosis, which is characterised by destruction of normal lung architecture and excessive deposition of lung extracellular matrix. The heterogeneity of disease progression in patients with IPF poses significant obstacles to patient care and prevents efficient development of novel therapeutic interventions. Blood biomarkers, reflecting pathobiological processes in the lung, could provide objective evidence of the underlying disease. Longitudinally collected serum samples from the Bosentan Use in Interstitial Lung Disease (BUILD-3 trial were used to measure four biomarkers (metalloproteinase-7 (MMP-7, Fas death receptor ligand, osteopontin and procollagen type I C-peptide, to assess their potential prognostic capabilities and to follow changes during disease progression in patients with IPF. In baseline BUILD-3 samples, only MMP-7 showed clearly elevated protein levels compared with samples from healthy controls, and further investigations demonstrated that MMP-7 levels also increased over time. Baseline levels of MMP-7 were able to predict patients who had higher risk of worsening and, notably, baseline levels of MMP-7 could predict changes in FVC as early as month 4. MMP-7 shows potential to be a reliable predictor of lung function decline and disease progression.

  13. Predicting the short-term risk of diabetes in HIV-positive patients

    DEFF Research Database (Denmark)

    Petoumenos, Kathy; Worm, Signe Westring; Fontas, Eric

    2012-01-01

    Introduction: HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM ......). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV-related factors, recent CD4 counts of...... and other glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

  14. A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

    Science.gov (United States)

    Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

    2015-12-01

    Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is

  15. Identifying noncoding risk variants using disease-relevant gene regulatory networks.

    Science.gov (United States)

    Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

    2018-02-16

    Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

  16. Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

    Science.gov (United States)

    Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-20

    Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

  17. Cardiovascular risk factors and future risk of Alzheimer's disease

    NARCIS (Netherlands)

    R.F.A.G. de Bruijn (Renée); M.A. Ikram (Arfan)

    2014-01-01

    textabstractAlzheimer's disease (AD) is the most common neurodegenerative disorder in elderly people, but there are still no curative options. Senile plaques and neurofibrillary tangles are considered hallmarks of AD, but cerebrovascular pathology is also common. In this review, we summarize

  18. Social and Behavioral Risk Marker Clustering Associated with Biological Risk Factors for Coronary Heart Disease: NHANES 2001–2004

    Directory of Open Access Journals (Sweden)

    Nicholas J. Everage

    2014-01-01

    Full Text Available Background. Social and behavioral risk markers (e.g., physical activity, diet, smoking, and socioeconomic position cluster; however, little is known whether clustering is associated with coronary heart disease (CHD risk. Objectives were to determine if sociobehavioral clustering is associated with biological CHD risk factors (total cholesterol, HDL cholesterol, systolic blood pressure, body mass index, waist circumference, and diabetes and whether associations are independent of individual clustering components. Methods. Participants included 4,305 males and 4,673 females aged ≥20 years from NHANES 2001–2004. Sociobehavioral Risk Marker Index (SRI included a summary score of physical activity, fruit/vegetable consumption, smoking, and educational attainment. Regression analyses evaluated associations of SRI with aforementioned biological CHD risk factors. Receiver operator curve analyses assessed independent predictive ability of SRI. Results. Healthful clustering (SRI = 0 was associated with improved biological CHD risk factor levels in 5 of 6 risk factors in females and 2 of 6 risk factors in males. Adding SRI to models containing age, race, and individual SRI components did not improve C-statistics. Conclusions. Findings suggest that healthful sociobehavioral risk marker clustering is associated with favorable CHD risk factor levels, particularly in females. These findings should inform social ecological interventions that consider health impacts of addressing social and behavioral risk factors.

  19. Increased risk of sudden cardiac arrest in obstructive pulmonary disease

    DEFF Research Database (Denmark)

    Warnier, Miriam Jacoba; Blom, Marieke Tabo; Bardai, Abdennasser

    2013-01-01

    BACKGROUND: We aimed to determine whether (1) patients with obstructive pulmonary disease (OPD) have an increased risk of sudden cardiac arrest (SCA) due to ventricular tachycardia or fibrillation (VT/VF), and (2) the SCA risk is mediated by cardiovascular risk-profile and/or respiratory drug use...... with electrocardiographic documentation of VT/VF were included. Conditional logistic regression analysis was used to assess the association between SCA and OPD. Pre-specified subgroup analyses were performed regarding age, sex, card